Andre Peremans | Operations Research | Best Researcher Award

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Dr. Andre Peremans | Operations Research | Best Researcher Award

Department of Physics at University of Namur | Belgium

Dr. André Peremans is a distinguished Belgian physicist recognized for his pioneering contributions in laser-based spectroscopy, surface science, and nonlinear optical techniques. He obtained his Ph.D. in Science in 1990 from the Facultés Universitaires Notre-Dame de la Paix, Belgium, where his dissertation focused on resonant infrared laser stimulation and molecular desorption, and later earned the Agrégation de l’Enseignement Supérieur in 2004, underscoring his academic leadership. Over the years, he has held key research positions with the Belgian National Fund for Scientific Research (FNRS), progressing from Research Associate to Research Director, and gained valuable international experience through collaborations at the University of Tokyo, Paris-Sud (LURE), and SUNY Buffalo. His professional career is defined by expertise in nonlinear vibrational spectroscopy, sum-frequency generation (SFG), and the development of advanced laser systems for exploring electrochemical interfaces, biomolecular recognition, and biosensors. Dr. Peremans has demonstrated exceptional research skills in spectroscopy, biophotonics, and laser diagnostics, producing more than 60 high-impact publications in leading journals such as Physical Review Letters, Journal of Physical Chemistry, Chemical Physics Letters, Optics Letters, and Biosensors and Bioelectronics, along with multiple book chapters that have influenced physics, chemistry, and materials science. His innovative work is further reflected in international patents on laser technologies and spectroscopy, bridging fundamental research with applied solutions. In addition to his scientific output, he has supervised several Ph.D. theses, contributing significantly to the training and mentorship of early-career researchers. His achievements have been recognized through prestigious honors, including the French Community Award for his Ph.D. dissertation in 1990 and the Economic Prize of the Province of Namur in 1999. In conclusion, Dr. Peremans stands as an influential figure in physics and spectroscopy, whose leadership, innovation, and international collaborations continue to shape cutting-edge research and foster advancements with profound scientific and societal impact.

Profile: Scopus

Publications

1. Evaluation of table-top lasers for routine infrared ion spectroscopy in the analytical laboratory. (2021). Analyst. [Citations: 13]

2. Assessment of disinfection potential of Q-switch Nd:YAG laser on contaminated titanium implant surfaces. (2021). Materials. [Citations: 7]

3. Super resolution far field infrared microscopy [Conference paper]. (2020). [Citations: 1]

4. Q-Switch Nd:YAG laser-assisted elimination of multi-species biofilm on titanium surfaces. (2020). Materials. [Citations: 14]

5. Q-Switch Nd:YAG laser-assisted decontamination of implant surface. (2019). Dentistry Journal. [Citations: 17]

Muhammad Yousaf Iqbal | Optimization | Best Scholar Award

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Dr. Muhammad Yousaf Iqbal | Optimization | Best Scholar Award

Research Assistant at Zhejiang University of Technology, China

Dr. Muhammad Yousaf Iqbal is a Mechanical Engineer and Postdoctoral Researcher at Zhejiang University of Technology, specializing in vibration-based virtual sensors, RCCI engines, renewable energy systems, and advanced combustion monitoring. He earned his Ph.D. in Mechanical Engineering from Taiyuan University of Technology, with expertise spanning vehicle engineering, automotive design, vibration analysis, and energy regenerative systems. His research portfolio includes projects on hydrogen–diesel RCCI engines, methanol-blended fuels, and regenerative hydraulic electromagnetic shock absorbers, demonstrating a strong link between academic innovation and industrial application. He has authored more than 18 peer-reviewed articles in Q1–Q3 SCI/Scopus indexed journals, co-authored a technical book, and contributed as a reviewer and editorial member with IEEE. With over 230 citations, leadership in international collaborations, and active involvement in student organizations, he continues to advance sustainable automotive and mechanical research globally.

Professional Profile

Google Scholar 

Education

Dr. Muhammad Yousaf Iqbal completed his Ph.D. in Mechanical Engineering from Taiyuan University of Technology, focusing on vibration-based virtual sensors, RCCI engines, and sustainable energy systems. His doctoral research centered on combustion monitoring and virtual sensing technologies to enhance efficiency and reduce emissions in advanced engines. Prior to his Ph.D., he built a strong academic foundation in mechanical and automotive engineering, acquiring expertise in thermodynamics, heat transfer, and mechanical design. His academic training combined rigorous theoretical studies with practical industrial exposure, enabling him to develop advanced problem-solving skills and interdisciplinary knowledge. The breadth of his education reflects a commitment to combining innovation with real-world applications, laying the groundwork for his postdoctoral research at Zhejiang University of Technology and his continuous contributions to mechanical and energy engineering fields.

Experience

Dr. Muhammad Yousaf Iqbal possesses extensive academic, research, and industrial experience, combining over seven years of professional engagement across mechanical and automotive domains. As a Postdoctoral Researcher at Zhejiang University of Technology, he contributes to international collaborations and innovative research projects in vibration analysis, RCCI engines, and regenerative systems. His prior involvement includes doctoral research in advanced combustion systems and hands-on training with Dayun Truck Industry in vehicle design and assembly. He has successfully balanced teaching, mentoring, and supervising student research while actively contributing to the scientific community as a reviewer and editorial board member with IEEE. His experience demonstrates the ability to integrate academic rigor with industrial applications, ensuring impactful outcomes in automotive design, renewable energy solutions, and mechanical engineering innovations with strong global relevance.

Research Interest

Dr. Muhammad Yousaf Iqbal’s research interests span vibration-based virtual sensing, advanced combustion technologies, and sustainable energy systems. He specializes in the optimization of RCCI engines through alternative fuels, including hydrogen, diesel, and methanol blends, aiming to improve efficiency while reducing emissions. His work extends to developing energy regenerative hydraulic electromagnetic shock absorbers that contribute to energy recovery in vehicles, as well as projects involving automotive design, vehicle dynamics, and propulsion systems. He is equally engaged in mechanical design, thermodynamics, heat transfer, and composite materials, ensuring a multidisciplinary approach to innovation. His research bridges the gap between theory and practice, contributing to both academic advancements and industrial solutions. These interests reflect his long-term vision of fostering sustainable technologies for cleaner and more efficient mechanical and automotive engineering applications.

Award and Honor

Dr. Muhammad Yousaf Iqbal has been recognized for his significant contributions to research and innovation through multiple awards and honors. He has published more than 18 peer-reviewed articles in Q1–Q3 SCI/Scopus indexed journals, a recognition of his scientific rigor and dedication to impactful research. His academic achievements are complemented by international collaborations with prestigious research groups, earning him acknowledgment within the global research community. He co-authored a technical book on 3D Mechanical Design, which highlights his dedication to advancing engineering knowledge. His involvement with IEEE as an editorial member further adds to his professional recognition. These distinctions reflect his excellence in research, teaching, and leadership, underscoring his growing influence in mechanical and energy engineering while reinforcing his potential for continued recognition in future academic and professional endeavors.

Research Skill

Dr. Muhammad Yousaf Iqbal demonstrates advanced research skills in mechanical design, vibration analysis, thermodynamics, and combustion monitoring. He is proficient in developing vibration-based virtual sensors, enabling cost-effective real-time monitoring of NOx emissions in diesel and RCCI engines. His expertise includes experimental design, computational modeling, and data analysis for automotive and energy systems. Skilled in working with renewable and alternative fuels, he has contributed to optimizing hydrogen–diesel RCCI engines and investigating methanol fuel blends. He combines theoretical understanding with practical engineering skills, integrating design, manufacturing, and simulation to deliver innovative solutions. His cross-cultural collaboration experience and project management abilities strengthen his role in international research projects. With a strong publication record and editorial contributions, he possesses the technical, analytical, and leadership skills essential for advancing mechanical and automotive research.

Publication Top Notes

  • Title: A double-channel hybrid deep neural network based on CNN and BiLSTM for remaining useful life prediction
    Authors: C Zhao, X Huang, Y Li, M Yousaf Iqbal
    Year: 2020
    Citation: 119

  • Title: A fractional Whitham-Broer-Kaup equation and its possible application to tsunami prevention
    Authors: Y Wang, YF Zhang, ZJ Liu, M Iqbal
    Year: 2017
    Citation: 23

  • Title: A study of advanced efficient hybrid electric vehicles, electric propulsion and energy source
    Authors: MY Iqbal, T Wang, G Li, D Chen, MM Al-Nehari
    Year: 2022
    Citation: 16

  • Title: Development and Validation of a Vibration-Based Virtual Sensor for Real-Time Monitoring NOx Emissions of a Diesel Engine
    Authors: MY Iqbal, T Wang, G Li, S Li, G Hu, T Yang, F Gu, M Al-Nehari
    Year: 2022
    Citation: 15

  • Title: A short review on analytical methods for fractional equations with He’s fractional derivative
    Authors: Y Wang, YF Zhang, JG Liu, M Iqbal
    Year: 2017
    Citation: 15

  • Title: Investigation of accumulator main parameters of hydraulic excitation system
    Authors: Z Wu, Y Xiang, M Li, MY Iqbal, G Xu
    Year: 2019
    Citation: 13

  • Title: Study of external characteristics of hydraulic electromagnetic regenerative shock absorber
    Authors: MY Iqbal, Z Wu, G Xu, SA Bukhari
    Year: 2019
    Citation: 7

  • Title: A High-Efficiency Energy Harvesting by Using Hydraulic Electromagnetic Regenerative Shock Absorber
    Authors: MY Iqbal, Z Wu, W Tie, G Li, J Zhiyong, H GuiCheng
    Year: 2020
    Citation: 6

  • Title: Improving the effect of air chambers on micro-pressure waves from tunnel portals: Moderate underdamping
    Authors: F Liu, H Lei, M Wei, H Sun, MY Iqbal, D Chen
    Year: 2024
    Citation: 5

  • Title: A Triboelectric Piston–Cylinder Assembly with Condition‐Monitoring and Self‐Powering Capabilities
    Authors: G Li, H Wu, R Guo, H Zhang, L Li, MY Iqbal, F Gu
    Year: 2022
    Citation: 4

  • Title: Grinding mechanism of high-temperature nickel-based alloy using FEM-FBM technique
    Authors: M Al-Nehari, G Liang, L Ming, W Yahya, A Algaradi, MY Iqabal
    Year: 2021
    Citation: 4

  • Title: Cavitation Failure Analysis of Cylinder Liner in Diesel Engines Caused by Increased Combustion Pressure
    Authors: P Liu, R Tan, L Li, W Shi, J Chen, MY Iqbal, D Liu, G Li
    Year: 2024
    Citation: 1

  • Title: Combustion Parametric Investigations of Methanol-Based RCCI Internal Combustion Engine and Comparison with the Conventional Dual Fuel Mode
    Authors: MY Iqbal, T Wang, GX Li, W Ali
    Year: 2023
    Citation: 1

  • Title: Performance comparison of switching losses of SiC DMOS vs Si IGBT
    Authors: SA Bukhari, H Zhang, SH Bukhari, MY Iqbal
    Year: 2020
    Citation: 1

  • Title: Impact of Fischer-Tropsch diesel and methanol blended fuel on diesel engine performance
    Authors: Z Wu, T Wang, P Zuo, MY Iqbal
    Year: 2019
    Citation: 1

Conclusion

Dr. Muhammad Yousaf Iqbal stands out as an accomplished researcher whose work bridges academic innovation and industrial application. His expertise in vibration-based virtual sensing, RCCI engine optimization, and regenerative energy systems has contributed to advancements in sustainable automotive engineering. With extensive publications, international collaborations, and leadership in professional organizations, he has established himself as a rising figure in global research. His dedication to mentoring students, engaging in cross-disciplinary projects, and contributing as a reviewer and editorial member reflects both academic excellence and community involvement. Beyond his current achievements, he holds strong potential to lead pioneering projects that shape future technologies in clean energy and advanced mechanical systems. These contributions affirm his suitability for recognition, positioning him as a deserving candidate for prestigious research awards.

 

Seyed Mehdi Tavakkoli | Optimization | Best Researcher Award

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Assoc. Prof. Dr. Seyed Mehdi Tavakkoli | Optimization | Best Researcher Award

Associate Professor at Shahrood University of Technology, Iran

Dr. Seyed Mehdi Tavakkoli is a distinguished scholar in structural and computational engineering 🏗️📊, serving as Associate Professor at Shahrood University of Technology, Iran. With a Ph.D. in Civil Engineering – Structures from Iran University of Science & Technology 🎓, he has pioneered research in isogeometric analysis, structural topology optimization, and continuum mechanics. His prolific academic career includes over 30 peer-reviewed journal publications 📝, mentorship of MSc and Ph.D. students, and international collaboration at the University of Bath, UK 🌍. Dr. Tavakkoli is highly skilled in MATLAB, Python, and leading structural software tools 💻, blending theory with practical applications. His innovative contributions to damage detection, shape optimization, and smart materials position him as a leading voice in modern civil engineering 🔍🏅. He is deeply committed to advancing research, education, and global knowledge exchange.

Professional Profile 

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ORCID Profile

🎓 Education

Dr. Seyed Mehdi Tavakkoli holds a Ph.D. in Civil Engineering – Structures from Iran University of Science & Technology 🎓, where he specialized in isogeometric analysis and topology optimization using NURBS basis functions. Guided by renowned experts like Prof. Behrooz Hassani and Prof. Ali Kaveh, his dissertation laid the foundation for integrating computational geometry with structural optimization. His education blends strong mathematical rigor, numerical methods, and engineering design 🧠📐. Earlier degrees were equally grounded in high academic achievement, with a consistent focus on advanced mechanics and structural systems. His academic journey demonstrates a fusion of innovation and precision, making him an expert in structural modeling, finite elements, and smart structural systems. Dr. Tavakkoli’s education has positioned him as a forward-thinking engineer capable of solving complex multi-scale problems 🏗️🔍.

📊Professional Experience

Dr. Tavakkoli has over 15 years of academic and international research experience. He currently serves as an Associate Professor in the Civil Engineering Department at Shahrood University of Technology 🏛️, where he teaches undergraduate and postgraduate courses in structural analysis, finite element methods, and optimization. From 2013–2014, he worked as a Research Associate at the University of Bath, UK 🇬🇧, where he developed nonlinear models of piezoelectric-bistable laminates for energy harvesting. His teaching record is distinguished by the design of curricula focused on mechanics, computational methods, and structural systems. He has supervised numerous MSc and PhD theses, bringing theoretical insight into practical applications 📘👨‍🔧. His professional journey reflects a balanced commitment to academic excellence, innovation, and global collaboration 🌍📊.

🔬 Research Interests

Dr. Tavakkoli’s research lies at the intersection of computational mechanics, structural optimization, and smart materials. He specializes in isogeometric analysis, finite element modeling, and topology optimization of structures, particularly for energy-efficient and damage-tolerant design systems 🧮⚙️. His recent studies include multiscale modeling, couple stress theory, level-set methods, and elastoplastic material behavior in structural systems. He has also contributed to damage detection using time-domain responses and modal expansion methods. His work is regularly published in high-impact journals like Engineering with Computers, Computer-Aided Design, and Finite Elements in Analysis and Design 📑📈. Passionate about integrating theory and real-world solutions, his research advances fields such as additive manufacturing, structural health monitoring, and sustainable civil engineering systems 🌱🏗️.

🏅 Awards and Honors

Dr. Tavakkoli’s sustained contributions to civil and structural engineering have earned him recognition as a prolific researcher in computational design and optimization. While formal award titles are not explicitly listed, his continuous publication in top-tier international journals and collaborations with leading institutions, including the University of Bath, reflect his scholarly impact 🏆🌍. His research papers—cited widely in structural optimization and computational mechanics—have influenced both academia and industry. His role as a supervisor and project leader in complex optimization frameworks also underlines his academic leadership. The innovative nature of his work in isogeometric topology optimization is gaining increasing recognition across structural engineering circles, positioning him as a thought leader in the evolving landscape of smart and sustainable design systems 🧠🛠️.

🛠️ Research Skills

Dr. Tavakkoli possesses deep expertise in structural modeling, numerical methods, and advanced optimization techniques. He is proficient in programming languages such as MATLAB, Python, Visual Fortran, and Visual Basic 👨‍💻. His computational skillset includes structural analysis software like ETABS, SAP2000, and SAFE, alongside core platforms like AutoCAD and MS Office. His research utilizes isogeometric and finite element methods, multiscale modeling, and optimization algorithms including ant colony and level-set techniques 🔄📊. His capabilities also extend to damage detection and nonlinear modeling of smart materials. This combination of practical software fluency and high-level theoretical modeling makes him highly adaptable for modern engineering challenges—especially in the development of smart, safe, and efficient structural systems ⚡🏗️.

📝Publications Top Note

  • Title: Structural topology optimization using ant colony methodology
    Authors: A. Kaveh, B. Hassani, S. Shojaee, S.M. Tavakkoli
    Year: 2008
    Citations: 210
    Source: Engineering Structures, 30(9), 2559–2565

  • Title: An isogeometrical approach to structural topology optimization by optimality criteria
    Authors: B. Hassani, M. Khanzadi, S.M. Tavakkoli
    Year: 2012
    Citations: 169
    Source: Structural and Multidisciplinary Optimization, 45(2), 223–233

  • Title: Simultaneous shape and topology optimization of shell structures
    Authors: B. Hassani, S.M. Tavakkoli, H. Ghasemnejad
    Year: 2013
    Citations: 100
    Source: Structural and Multidisciplinary Optimization, 48(1), 221–233

  • Title: An isogeometrical approach to structural level set topology optimization
    Authors: H.A. Jahangiri, S.M. Tavakkoli
    Year: 2017
    Citations: 88
    Source: Computer Methods in Applied Mechanics and Engineering, 319, 240–257

  • Title: Application of isogeometric analysis in structural shape optimization
    Authors: B. Hassani, S.M. Tavakkoli, N.Z. Moghadam
    Year: 2011
    Citations: 74
    Source: Iranian Science, 18(4), 846–852

  • Title: An isogeometrical approach to error estimation and stress recovery
    Authors: B. Hassani, A. Ganjali, M. Tavakkoli
    Year: 2012
    Citations: 37
    Source: European Journal of Mechanics A: Solids, 31(1), 101–109

  • Title: Isogeometric shape optimization of three dimensional problems
    Authors: B. Hassani, M. Khanzadi, S.M. Tavakkoli, N.Z. Moghadam
    Year: 2009
    Citations: 33
    Source: 8th World Congress on Structural and Multidisciplinary Optimization, 1–5

  • Title: Isogeometric topology optimization of structures by using MMA
    Authors: S.M. Tavakkoli, B. Hassani, H. Ghasemnejad
    Year: 2013
    Citations: 27
    Source: International Journal of Optimization in Civil Engineering, 3(2), 313–326

  • Title: Free vibration of functionally graded thick circular plates: An exact and three-dimensional solution
    Authors: M.Z. Roshanbakhsh, S.M. Tavakkoli, B.N. Neya
    Year: 2020
    Citations: 25
    Source: International Journal of Mechanical Sciences, 188, 105967

  • Title: Structural damage detection in plane stress problems by using time domain responses and topology optimization
    Authors: F. Damghani, S.M. Tavakkoli
    Year: 2023
    Citations: 2
    Source: International Journal of Optimization in Civil Engineering (IJOCE), 13(2)

  • Title: An analytical study on piezoelectric-bistable laminates with arbitrary shapes for energy harvesting
    Authors: S.M. Tavakkoli, P.M. Weaver, C.R. Bowen, D.J. Inman, H. Alicia
    Year: 2015
    Citations: 2
    Source: [Not specified, likely conference proceedings or journal]

  • Title: Topology optimization of space structures using ant colony method
    Authors: S.M. Tavakkoli, L. Shahryari, A. Parsa
    Year: 2013
    Citations: 2
    Source: International Journal of Optimization in Civil Engineering, 3(3), 359–370

  • Title: Channels flow modeling by using isogeometric analysis
    Authors: R. Amini, R. Maghsoodi, N.Z. Moghaddam, S.M. Tavakkoli
    Year: 2016
    Citations: 1
    Source: Journal of Solid and Fluid Mechanics, 5(4)

  • Title: Size-dependent topology optimization for eigenfrequency maximization of microplates using consistent couple stress theory
    Authors: M.Z. Roshanbakhsh, S.M. Tavakkoli
    Year: 2025
    Source: Advances in Engineering Software, 206, 103941

🧾 Conclusion

Dr. Seyed Mehdi Tavakkoli exemplifies a rare blend of academic depth, technical precision, and forward-thinking research in structural engineering. With over three decades of cumulative experience across teaching, research, and international collaboration, he has become a key contributor to the evolving landscape of computational civil engineering 📘🌐. His isogeometric approach, grounded in rigorous mathematics and real-world applications, continues to influence emerging scholars and professionals alike. Whether through supervising doctoral candidates, publishing high-impact research, or exploring optimization in additive manufacturing, his work consistently pushes the boundary of what’s possible in engineering design. Dr. Tavakkoli stands out not only as a researcher but as a mentor, innovator, and thought leader of 21st-century structural systems 🏅🚀.

Lluis Miquel | Operations Research | Mathematical Modeling Breakthrough Award

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Prof. Dr. Lluis Miquel | Operations Research | Mathematical Modeling Breakthrough Award

Departamento Matemática at Pla-Aragones Universidad de Lleida, Spain

Dr. Luis Miguel Plà-Aragonès 🇪🇸 is a renowned expert in Operations Research 🔍 and Decision Analysis 🧠, with a focus on applications in agriculture 🌾, economics 💹, and health systems 🏥. As a professor at the University of Lleida, Spain, he has made significant contributions through multi-criteria decision-making, optimization modeling, and policy analysis. His interdisciplinary approach bridges the gap between theory and real-world impact, particularly in areas like agricultural planning, resource allocation, and cost-effectiveness analysis. 📊 With numerous publications 📚 and collaborations across Europe and Latin America 🌍, Dr. Plà-Aragonès is recognized for advancing the role of decision science in solving complex societal challenges. He is a dedicated mentor 👨‍🏫, respected academic, and a driving force behind the integration of quantitative models into sustainable decision-making practices. ♻️

Professional Profile 

Google Scholar
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ORCID Profile

🎓 Education

Dr. Luis Miguel Plà-Aragonès holds a Ph.D. in Agricultural Engineering 🧑‍🔬 from the University of Lleida, Spain 🇪🇸, where he also earned his undergraduate and master’s degrees. His academic journey has been deeply rooted in applying mathematical and decision-analytic tools 📐 to complex agricultural systems 🌾. With a strong foundation in statistics, optimization, and operations research, he further enriched his expertise through postdoctoral training and international research exchanges across Europe 🌍. His educational background blends technical rigor with practical insight, laying the groundwork for his interdisciplinary research. His early academic excellence and commitment to innovation have made him a respected scholar in quantitative decision-making 🧠, particularly within the agricultural and environmental sectors. 📊 His educational path has continuously evolved toward bridging scientific research and real-world problem solving. 🧑‍🏫

💼 Professional Experience

Dr. Plà-Aragonès serves as a Professor of Operations Research and Decision Analysis at the University of Lleida 🏛️, with more than two decades of academic experience 👨‍🏫. He has held leadership roles in multiple international research networks, including coordinating the EURO Working Group on Operational Research in Agriculture and Forest Management 🌳. His career spans collaborative projects with both academic and industry stakeholders, particularly in the pig and dairy farming sectors 🐖🐄. As a principal investigator, he has led EU and Latin American research consortia in modeling and AI-driven agricultural innovation 🤖. He also mentors Ph.D. students and has developed decision-support systems used in real-world agribusiness. His dynamic professional profile showcases a balance of teaching, research, consultancy, and technological transfer across interdisciplinary domains. 🌍

🔬 Research Interests

Dr. Plà-Aragonès’s research revolves around mathematical modeling, multi-criteria decision-making, and operations research applied to agriculture, healthcare, and resource management 🌾🏥📊. He is especially known for his work in livestock logistics, sow replacement optimization, and agri-food supply chain modeling using Markov Decision Processes, stochastic optimization, and AI-enhanced systems 🤖. His interests also extend to cost-effectiveness analysis in healthcare, bridging economic modeling with decision science 💊. He is a key advocate for integrating AI, cloud computing ☁️, and IoT into agricultural modeling, ensuring smarter and more sustainable farm management. Through international collaborations 🌍 and cross-sector applications, his research continually addresses real-world challenges in food systems, environmental sustainability ♻️, and public health policy using rigorous quantitative methods. 📈

🏅 Awards and Honors

Dr. Luis Miguel Plà-Aragonès has been honored for his outstanding contributions to operations research and agricultural modeling 🌍. He is a recipient of various research leadership recognitions, including the prestigious coordination role in the CYTED BigDSSAgro Network and awards from international agricultural and engineering bodies 🧪. His work has earned accolades for bridging research and practice in farming systems, and he has been frequently invited as a keynote speaker 🎤 at global conferences in AI for agriculture and decision support systems. Under his leadership, several European Union and international collaborative projects have won funding and academic praise 💼💡. His active role in mentoring, publishing, and innovating across disciplines has made him a respected and decorated figure in decision science and sustainable development. 🥇

🧰 Research Skills

Dr. Plà-Aragonès possesses advanced skills in mathematical modeling, optimization, stochastic processes, and simulation modeling 🔢. He is adept in applying Markov Decision Processes, Multi-Criteria Decision Analysis (MCDA), and AI algorithms to develop decision-support tools for agriculture and health sectors 🌱🏥. His programming capabilities span R, Python, and MATLAB, while his modeling expertise extends to GAMS, LINGO, and AnyLogic 💻. He’s proficient in integrating data analytics, machine learning, and cloud-based architectures for developing scalable, digital decision systems ☁️🤖. Dr. Plà’s multidisciplinary skills allow him to lead cross-border research, publish in top-tier journals 📚, and translate theory into practical tools for industry. His ability to synthesize quantitative methods into actionable solutions defines his technical excellence. ⚙️

Publications Top Note 📝

  • Title: Operational research models applied to the fresh fruit supply chain
    Authors: WE Soto-Silva, E Nadal-Roig, MC González-Araya, LM Plà-Aragonès
    Year: 2016
    Citations: 337
    Source: European Journal of Operational Research, 251(2), 345-355

  • Title: Sugar cane transportation in Cuba, a case study
    Authors: EL Milan, SM Fernandez, LMP Aragones
    Year: 2006
    Citations: 132
    Source: European Journal of Operational Research, 174(1), 374-386

  • Title: Optimizing fresh food logistics for processing: Application for a large Chilean apple supply chain
    Authors: WE Soto-Silva, MC González-Araya, MA Oliva-Fernández, et al.
    Year: 2017
    Citations: 123
    Source: Computers and Electronics in Agriculture, 136, 42-57

  • Title: A perspective on operational research prospects for agriculture
    Authors: LM Plà, DL Sandars, AJ Higgins
    Year: 2014
    Citations: 101
    Source: Journal of the Operational Research Society, 65(7), 1078–1089

  • Title: Review of mathematical models for sow herd management
    Authors: LM Plà
    Year: 2007
    Citations: 66
    Source: Livestock Science, 106(2–3), 107–119

  • Title: New opportunities in operations research to improve pork supply chain efficiency
    Authors: SV Rodríguez, LM Plà, J Faulin
    Year: 2014
    Citations: 63
    Source: Annals of Operations Research, 219, 5–23

  • Title: A Markov decision sow model representing the productive lifespan of herd sows
    Authors: LM Plà, C Pomar, J Pomar
    Year: 2003
    Citations: 53
    Source: Agricultural Systems, 76(1), 253–272

  • Title: Environmental assessment of a pork-production system in North-East of Spain focusing on life-cycle swine nutrition
    Authors: C Lamnatou, X Ezcurra-Ciaurriz, D Chemisana, LM Plà-Aragonès
    Year: 2016
    Citations: 52
    Source: Journal of Cleaner Production, 137, 105–115

  • Title: Optimal transport planning for the supply to a fruit logistic centre
    Authors: E Nadal-Roig, LM Plà-Aragonès
    Year: 2015
    Citations: 51
    Source: Handbook of Operations Research in Agriculture and the Agri-Food Industry

  • Title: Modeling tactical planning decisions through a linear optimization model in sow farms
    Authors: SV Rodríguez-Sánchez, LM Plà-Aragonès, VM Albornoz
    Year: 2012
    Citations: 50
    Source: Livestock Science, 143(2–3), 162–171

  • Title: Production planning of supply chains in the pig industry
    Authors: E Nadal-Roig, LM Plà-Aragonès, A Alonso-Ayuso
    Year: 2019
    Citations: 42
    Source: Computers and Electronics in Agriculture, 161, 72–78

  • Title: A two-stage stochastic programming model for scheduling replacements in sow farms
    Authors: SV Rodríguez, VM Albornoz, LM Plà
    Year: 2009
    Citations: 42
    Source: TOP, 17, 171–189

  • Title: Handbook of operations research in agriculture and the agri-food industry
    Author: LM Plà-Aragonès
    Year: 2015
    Citations: 41
    Source: Springer New York

  • Title: Selection of slaughterhouse to deliver fattened pigs depending on growth curves
    Authors: Y Bao, P Llagostera, D Babot, LM Plà-Aragonès
    Year: 2025
    Source: Agricultural Systems, 229, 104406

  • Title: Mathematical methods applied to the problem of dairy cow replacements: a scoping review
    Authors: O Palma, LM Plà-Aragonès, A Mac Cawley, VM Albornoz
    Year: 2025
    Source: Animals, 15(7), 970

  • Title: A genetic algorithm for site-specific management zone delineation
    Authors: F Huguet, LM Plà-Aragonès, VM Albornoz, M Pohl
    Year: 2025
    Source: Mathematics, 13(7), 1064

  • Title: A deep learning approach for image analysis and reading body weight from digital scales in pigs farms
    Authors: NA Reyes-Reyes, MC Doja, P Llagostera-Blasco, LM Plà-Aragonès, et al.
    Year: 2025
    Source: IEEE Access

  • Title: Mathematical Methods Applied to the Problem of Dairy Cow Replacements: A Scoping Review
    Authors: O Palma, LM Plà-Aragonès, A Mac Cawley, VM Albornoz
    Year: 2025
    Source: System, 26, 11

🧭 Conclusion

Dr. Luis Miguel Plà-Aragonès exemplifies the fusion of theoretical innovation and practical impact in mathematical modeling 🌍. With a foundation built on rigorous education 🎓 and two decades of professional excellence 💼, he has become a global leader in using operations research to solve real-world problems in agriculture, environment, and health 🌾💊♻️. His interdisciplinary collaborations, influential publications, and award-winning leadership reflect a visionary commitment to data-driven decision-making 📈. By integrating AI, cloud systems, and analytics into sustainable frameworks, Dr. Plà is shaping the future of intelligent agriculture and policy modeling 🤖. His dedication to mentorship, international outreach, and technological innovation makes him not only a researcher of high distinction but also a catalyst for global scientific progress. 🏆

Bin Wang | Operations Research | Excellence in Innovation

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Prof. Dr. Bin Wang | Operations Research | Excellence in Innovation

Professor at Anhui University of Chinese Medicine, China

Dr. Bin Wang 🎓, a distinguished researcher at the Key Laboratory of Xin’an Medicine, Anhui University of Chinese Medicine 🏛️, specializes in the scientific exploration of Traditional Chinese Medicine (TCM) 🌿. With a Ph.D. in Organic Chemistry from the University of Science and Technology of China 🧪, his innovative work focuses on the identification, structural analysis, and quality control of immunologically active polysaccharides. Dr. Wang has published extensively in high-impact journals 📚, contributing over 15 peer-reviewed articles that bridge traditional remedies with modern analytical techniques. His research enhances the functional food potential and pharmacological understanding of herbal medicines. Passionate about integrating chemistry, medicine, and health 🌱⚗️, Dr. Wang’s work is shaping the future of evidence-based TCM. His dedication and scientific excellence make him a valuable contributor to global health innovation 🌍💡.

Professional Profile 

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🎓 Education

Dr. Bin Wang earned his Ph.D. in Organic Chemistry 🧪 from the University of Science and Technology of China, under the mentorship of Prof. Zhiyong Wang. Prior to that, he completed his M.S. in Physical Chemistry ⚛️ at Hunan University with Prof. Bingxin Cai and his B.S. in Chemistry 🧫 from West Anhui University. His solid academic foundation spans organic synthesis, analytical methods, and chemical applications in traditional medicine. With a cross-disciplinary background, Dr. Wang has consistently blended theory with real-world medicinal chemistry, setting the stage for his career in Traditional Chinese Medicine (TCM) research. This strong educational journey reflects his commitment to scientific rigor and innovation in the biochemical understanding of herbal medicine 🌿🔬.

 🏛️ Professional Experience

Currently serving at the Key Laboratory of Xin’an Medicine, Ministry of Education, Anhui University of Chinese Medicine 🏛️, Dr. Bin Wang plays a pivotal role in advancing analytical methods for TCM. As a research leader, he directs high-impact studies focused on polysaccharide identification and bioactivity analysis 📈. Over the years, he has collaborated with leading pharmacologists, chemists, and clinicians, bridging traditional herbal wisdom with modern scientific validation. Dr. Wang’s career reflects his strong academic leadership, lab excellence, and mentorship of emerging scholars 🧑‍🔬. His expertise is also frequently sought after in interdisciplinary projects and national-level TCM development initiatives, contributing to policy, product innovation, and public health impact across China 🇨🇳🔍.

🔬 Research Interest

Dr. Wang’s research lies at the intersection of natural product chemistry and bioactive polysaccharides from traditional Chinese medicinal herbs 🌿. His key interests include the structural elucidation of complex sugars, their pharmacological effects, and quality control of herbal ingredients using modern chromatographic and spectroscopic tools 💊📊. He’s particularly known for applying advanced techniques like UHPLC-QTOF/MS, response surface methodology, and chemometric modeling to evaluate antioxidant, anti-inflammatory, and immune-modulating properties. With a focus on TCM modernization, Dr. Wang aims to unlock therapeutic pathways and improve herbal drug standardization for global health applications 🌍⚗️. His work not only improves clinical outcomes but also supports the credibility of traditional practices in contemporary pharmacological science.

🏅 Awards & Honors

Dr. Wang’s contributions have earned him widespread recognition 🏆 across the fields of TCM and analytical chemistry. He has co-authored impactful papers in renowned journals such as Frontiers in Nutrition, RSC Advances, and International Journal of Biological Macromolecules 📚. His polysaccharide research has been instrumental in achieving breakthrough formulations and enhanced bioactivity testing methods. Though not all honors may be publicly listed, his consistent presence in high-indexed publications, involvement in critical national research projects, and peer respect underscore his role as an innovator 🌟. His growing citation record and influence in the modernization of TCM validate his candidacy for honors such as “Excellence in Innovation” and other research distinction awards 🥇.

🧠 Research Skills

Dr. Wang possesses a comprehensive skill set that spans organic synthesis, natural product extraction, mass spectrometry, and chromatographic fingerprinting 🔬. He is highly proficient in using HPLC, UV, UHPLC-QTOF/MS, LC-MS/MS, and chemometric tools to analyze the structural and functional properties of TCM polysaccharides. His strong command over statistical validation techniques (like response surface methodology and Lambert–Beer Law integration 📈) has made his analytical protocols highly reproducible. Beyond lab work, he excels at scholarly writing, cross-disciplinary team leadership, and method development for quality assurance in herbal pharmacology 🌿💼. These skills enable him to transform ancient remedies into scientifically verified therapeutics, making him a torchbearer for evidence-based traditional medicine 🌟.

Publications Top Note 📝

  • Discrimination of Polygonatum Species via Polysaccharide Fingerprinting: Integrating Their Chemometrics, Antioxidant Activity, and Potential as Functional Foods

    • Authors: Z. Liu, W. Zhang*, Bin Wang*

    • Year: 2025

    • Source: Foods, Article 14(13):2385. DOI: 10.3390/foods14132385

  • Mitsunobu Reaction: Assembling C–N Bonds in Chiral Traditional Chinese Medicine

    • Authors: X. Zhou, L. Xu, Z. Ma, J. Cui, B. Wang*

    • Year: 2025

    • Source: RSC Advances, Vol. 15, p. 5167–5189. DOI: 10.1039/D4RA08573F

  • Simultaneous Determination of Naphthalimide-Labeled Monosaccharides in P. cyrtonema Polysaccharides Utilizing HPLC‑UV

    • Authors: J. Du, X. Zhou, L. Chen, L. Xu, B. Wang*

    • Year: 2025

    • Source: Analytical Methods, Vol. 17, pp. 1196–1205.

  • Structural Characteristics and Biological Activity of a Water-Soluble Polysaccharide HDCP‑2 from Camellia sinensis

    • Authors: Q. Sun, J. Du, Z. Wang, X. Li, R. Fu, H. Liu, N. Xu*, G. Zhu*, B. Wang*

    • Year: 2024

    • Source: International Journal of Biological Macromolecules, 277:134437.

  • Structural Characterization and Antioxidant Activity of Processed Polysaccharides PCP‑F1 from Polygonatum cyrtonema

    • Authors: Y. Zhao, Z. Wang, R. Fu, R. Xie*, B. Wang*, Q. Li*

    • Year: 2023

    • Source: Frontiers in Nutrition, 10:1272977.

  • A Novel Method for Pre‑Column Derivatization of Saccharides from Polygonatum cyrtonema by Integrating Lambert–Beer Law and RSM

    • Authors: H. Liu, Y. Zhao, L. Chen, J. Du, H. Guo*, B. Wang*

    • Year: 2023

    • Source: Molecules, 28:2186.

  • Structural Characterization and Anti‑Inflammatory Activity of a Novel Polysaccharide PKP2‑1 from Polygonatum kingianum

    • Authors: Z. Wang, H. Liu, R. Fu, J. Ou*, B. Wang*

    • Year: 2023

    • Source: Frontiers in Nutrition, 10:1156798.

  • Comprehensive Evaluation and Anti‑Inflammatory Activity of “Zhi” Polygonatum cyrtonema Produced by Classical Steaming

    • Authors: Z. Wang, R. Xie*, B. Wang*

    • Year: 2023

    • Source: Pharmacological Research – Modern Chinese Medicine, 6:100229.

  • Synthesis of Naphthalimide-Type Chemosensor and Its Application in Quality Evaluation for Polygonatum sibiricum Red

    • Authors: Z. Wang, Q. Sun, Y. Zhao, J. Du, B. Wang*

    • Year: 2022

    • Source: Frontiers in Chemistry, 10:969014.

  • A Novel Method for Investigating the Mechanism of Anti‑Rheumatoid Arthritis Activity of Angelica pubescentis Radix by UHPLC–QTOF/MS & Network Pharmacology

    • Authors: Z. Wang, H. Liu, Y. Cao, T. Zhang, H. Guo*, B. Wang*

    • Year: 2022

    • Source: Biomedical Chromatography, 36:e5389.

  • Polygonatum sibiricum Polysaccharide Prevents Depression-Like Behaviors by Reducing Oxidative Stress, Inflammation, and Cellular & Synaptic Damage

    • Authors: F. Shen, Z. Song, P. Xie, L. Li, B. Wang*, D. Peng*, G. Zhu*

    • Year: 2021

    • Source: Journal of Ethnopharmacology, 275:114164.

  • Screening Q‑Markers of TCMs from RA Rat Plasma via UHPLC‑QTOF/MS for Wu-Wei-Wen-Tong Capsule

    • Authors: H. Jiang, J. Liu*, Y. Wang, L. Chen, H. Liu, Z. Wang, B. Wang*

    • Year: 2021

    • Source: Journal of Mass Spectrometry, 56:e4711.

  • Effects of Borneol on Release of Compound Danshen Colon-Specific Osmotic Pump Capsule: In Vitro & Beagle Pharmacokinetics

    • Authors: L. Shao, C. Sun, W. Lu, J. Chen, D. Su, S. Gao, S. Chen, W. Fang, Y. Liu, B. Wang*, R. Hu*

    • Year: 2020

    • Source: AAPS PharmSciTech, 21:316.

  • UHPLC‑UV + UHPLC‑QTOF/MS Fingerprint for Nao‑Luo‑Xin‑Tong: Multi‑Wavelength Setting for TCM Prescription Composition

    • Authors: L. Wang, Y. Wang, G. Tong, Y. Li, M. Lei, H. Wu, B. Wang*, R. Hu*

    • Year: 2019

    • Source: Analytical Methods, 11:6092.

  • Simultaneous Analysis of Coumarin Derivatives in Radix Angelicae pubescentis by HPLC‑DAD‑ESI‑MSn

    • Authors: B. Wang*, X. Liu, A. Zhou, M. Meng, Q. Li*

    • Year: 2014

    • Source: Analytical Methods, 6:7996–8002.

🧾 Conclusion

Dr. Bin Wang stands out as a visionary scientist whose work bridges tradition and innovation 🌿🔬. With a robust academic background, high-impact publications, and cutting-edge research in TCM polysaccharides, he’s making vital contributions to herbal medicine modernization. His integration of advanced analytical chemistry with clinical relevance ensures that traditional practices meet contemporary scientific standards ⚖️. As a mentor, researcher, and innovator, Dr. Wang embodies the excellence, curiosity, and impact that define 21st-century research leadership 🌍. He is a strong contender for honors like the Excellence in Innovation Award, reflecting his commitment to translational research and sustainable health science development 🌱🏅.

Jenny Tonsing | Operations Research | Best Researcher Award

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Assoc. Prof. Dr. Jenny Tonsing | Operations Research | Best Researcher Award

Associate professor/Social Work at Appalachian State University, United States

Dr. Jenny C. Tonsing 🎓 is a distinguished scholar and Associate Professor of Social Work at Appalachian State University, with a profound commitment to social justice, mental health, and refugee welfare 🌍. With a Ph.D. from Royal Holloway, University of London, and multiple master’s degrees, she has cultivated over a decade of international academic and research excellence 📚. Her impactful studies focus on domestic violence, psychological resilience, and cultural dynamics among marginalized communities, particularly South Asian and Burmese populations 🧠👩🏽‍👧🏽‍👦🏽. Dr. Tonsing has published prolifically in high-impact journals and presented at global conferences, earning respect as a thought leader in trauma-informed care and gender equity 🚺. She serves as a peer reviewer for numerous journals and is actively involved in shaping future social work practices through conferences and collaborative projects 🤝. Her scholarly voice bridges cultural insight with empirical rigor, making her a beacon in social work research and advocacy 💡🌱.

Professional Profile

Google Scholar
Scopus Profile
ORCID Profile

Education 🎓📘

Dr. Jenny C. Tonsing’s educational voyage is a testament to her relentless pursuit of knowledge and social impact. She earned her Ph.D. in Social Work from Royal Holloway, University of London 🇬🇧, following a robust academic foundation that includes two master’s degrees—one in Social Work and another in Psychology. Her educational path is marked by interdisciplinary learning and global exposure, fostering her expertise in cross-cultural social issues 🌏. She also holds certifications in trauma-informed practice and mental health, reflecting her dedication to bridging theory with real-world transformation 🧠✨. Her academic accomplishments are not only deeply rooted in research excellence but also in cultivating empathy, ethics, and advocacy. With a rich educational portfolio blending South Asian insight with Western scholarship, Dr. Tonsing has emerged as an intellectual pioneer equipped to challenge systemic inequities through socially conscious inquiry and teaching 🧑🏽‍🏫📖.

Professional Experience 👩🏽‍🏫🌍

Dr. Jenny C. Tonsing brings over a decade of dynamic professional experience in academia, social work, and community empowerment. Currently serving as an Associate Professor of Social Work at Appalachian State University 🏞️, she has previously held academic roles across continents, including Asia and the United Kingdom, which have deeply enriched her global perspective. Her roles extend beyond teaching—she is a mentor, a curriculum innovator, and a cross-cultural facilitator who bridges classroom theory with community practice 🤝💬. In addition to academic responsibilities, Dr. Tonsing has led numerous field initiatives focusing on refugee well-being, domestic violence prevention, and psychosocial support in vulnerable populations 🏠🧍🏽‍♀️🧍🏽‍♂️. Her ability to navigate diverse sociopolitical settings and adapt strategies for culturally relevant interventions underscores her as a pragmatic changemaker. Her professional legacy is rooted in compassion, resilience, and an unwavering commitment to social equity 🔍❤️.

Research Interests 🔬📊

Dr. Jenny C. Tonsing’s research pursuits are deeply woven into themes of trauma, gender equity, refugee resilience, and transnational identities 🌐👩🏽‍⚕️. She explores how individuals and communities navigate adversity within socio-cultural contexts, particularly focusing on South Asian and Burmese populations. Her work delves into pressing topics such as intimate partner violence, psychological well-being, migration stress, and culturally grounded mental health interventions 🧠💔🏳️. Combining qualitative depth with empirical clarity, she advocates for trauma-informed approaches that honor lived experiences while contributing to academic discourse. Dr. Tonsing’s studies not only identify systemic gaps but also propose actionable frameworks for social workers and policymakers alike 🧾📚. Her intersectional lens and collaborative methodologies make her scholarship both transformative and inclusive. Driven by a passion to inform policy and uplift underserved voices, she continues to challenge conventional paradigms in mental health and human rights research ⚖️💡.

Awards and Honors 🏅🎖️

Dr. Tonsing’s stellar contributions have earned her numerous accolades, cementing her as a leader in social work research and education. She has been honored for excellence in teaching, global engagement, and research innovation across her academic journey 🎓🌟. Her scholarly articles have received recognition in peer-reviewed journals, and she frequently receives invitations as a keynote speaker at international symposia and academic summits 🌍🎤. Moreover, she has been commended for her advocacy efforts in domestic violence prevention and refugee support, both in academic and humanitarian circles. As a reviewer for prestigious journals and a collaborator in funded projects, her professional reputation continues to flourish 🔍📈. These awards not only celebrate her academic merit but also reflect her commitment to societal change. Through each honor, Dr. Tonsing inspires both her students and peers to pursue justice, inclusivity, and intellectual rigor in all dimensions of social work 🌱👏.

Conclusion 🌟📌

Dr. Jenny C. Tonsing embodies the synergy of intellect, empathy, and advocacy. Her journey—from scholar to changemaker—reflects a rare dedication to illuminating the lives of marginalized communities through both education and action 🌏📚. Whether in the classroom, the field, or through rigorous research, she has consistently upheld a vision of equity, compassion, and evidence-based solutions 💼🧠. With an expansive academic background, rich professional experiences, and impactful scholarship, Dr. Tonsing continues to shape the landscape of social work globally. Her voice resonates in areas where silence once prevailed, championing the rights of the voiceless and paving pathways for systemic transformation 🚀🔊. As an educator, mentor, and advocate, she inspires a generation of socially conscious professionals to think critically, act ethically, and dream boundlessly 🌈🕊️. In every sphere she touches, Dr. Tonsing exemplifies what it means to lead with both heart and purpose.

Publications Top Notes

  • Title: Understanding the role of patriarchal ideology in intimate partner violence among South Asian women in Hong Kong
    Authors: JC Tonsing, KN Tonsing
    Year: 2019
    Citation: 124

  • Title: Psychological distress, coping and perceived social support in social work students
    Authors: M Vungkhanching, JC Tonsing, KN Tonsing
    Year: 2017
    Citation: 105

  • Title: Intimate partner violence in South Asian communities: Exploring the notion of ‘shame’ to promote understandings of migrant women’s experiences
    Authors: J Tonsing, R Barn
    Year: 2017
    Citation: 78

  • Title: Acculturation, perceived discrimination, and psychological distress: Experiences of South Asians in Hong Kong
    Authors: KN Tonsing, S Tse, JC Tonsing
    Year: 2016
    Citation: 43

  • Title: Domestic violence: Intersection of culture, gender, and context
    Authors: JC Tonsing
    Year: 2016
    Citation: 42

  • Title: Domestic violence, social support, coping and depressive symptomatology among South Asian women in Hong Kong
    Authors: KN Tonsing, JC Tonsing, T Orbuch
    Year: 2021
    Citation: 32

  • Title: Conceptualizing partner abuse among South Asian women in Hong Kong
    Authors: JC Tonsing
    Year: 2014
    Citation: 32

  • Title: Complexity of domestic violence in a South Asian context in Hong Kong: Cultural and structural impact
    Authors: JC Tonsing
    Year: 2016
    Citation: 17

  • Title: Exploring South Asian women’s experiences of domestic violence and help-seeking within the sociocultural context in Hong Kong
    Authors: KN Tonsing, JC Tonsing
    Year: 2019
    Citation: 16

  • Title: Help-seeking behaviors and practices among Fijian women who experience domestic violence: An exploration of the role of religiosity as a coping strategy
    Authors: J Tonsing, R Barn
    Year: 2021
    Citation: 13

  • Title: A study of domestic violence among South Asian women in Hong Kong
    Authors: J Tonsing
    Year: 2014
    Citation: 11

  • Title: A mixed-method study of stress and coping strategies among university social work students in the United States
    Authors: KN Tonsing, JC Tonsing
    Year: 2022
    Citation: 10

  • Title: Using ecological model to understand intimate partner violence
    Authors: J Tonsing
    Year: 2011
    Citation: 8

  • Title: A Study of Domestic Violence among the South Asian in Hong Kong
    Authors: JC Tonsing
    Year: 2010
    Citation: 8

  • Title: Prevalence and correlates of depressive symptoms among university students: A cross-sectional study
    Authors: KN Tonsing, JC Tonsing
    Year: 2023
    Citation: 6

  • Title: Fijian women’s experiences of domestic violence and mothers’ perceived impact of children’s exposure to abuse in the home
    Authors: J Tonsing
    Year: 2020
    Citation: 6

  • Title: Exploratory Study of the Resettlement Experiences of Burmese Refugees Children in the USA
    Authors: JC Tonsing
    Year: 2021
    Citation: 2

  • Title: Beliefs about mental health and barriers to psychological help-seeking among Burmese refugees: A mixed-method inquiry
    Authors: KN Tonsing, JC Tonsing
    Year: 2025
    Citation: 1

 

Shijie Zhao | Applied Mathematics | Best Researcher Award

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Assoc. Prof. Dr. Shijie Zhao | Applied Mathematics | Best Researcher Award

Associate Professor at Liaoning Technical University, China

Assoc. Prof. Dr. Shijie Zhao is a distinguished researcher and academic at the Institute of Intelligence Science and Optimization, Liaoning Technical University, China. With a Ph.D. in Optimization and Management Decisions, his expertise lies in metaheuristic optimization, multi-objective optimization, and underwater navigation and positioning. He has made significant contributions through innovative algorithm designs and novel mathematical models, particularly in high-dimensional feature selection and robust navigation techniques. Dr. Zhao has published 9 SCI-indexed journal articles and participated in over 10 nationally and provincially funded research projects. He serves as a reviewer for leading journals including those by Elsevier, Springer, and IEEE, and holds memberships in 13 professional bodies. With strong programming skills, rigorous analytical thinking, and a commitment to scientific innovation, Dr. Zhao has also earned four research awards. His work bridges theoretical mathematics and practical applications, making him a valuable contributor to the global research community in intelligent systems and optimization.

Professional Profile 

Scopus Profile
ORCID Profile

Education

Assoc. Prof. Dr. Shijie Zhao has a robust academic foundation anchored at Liaoning Technical University, China. He earned his B.S. degree in Science of Information & Computation in 2012, followed by a successive postgraduate and doctoral program in Mathematics and Applied Mathematics from 2012 to 2014. He went on to complete his Ph.D. in Optimization and Management Decisions in 2018. His educational trajectory highlights a deep commitment to the field of mathematical optimization and intelligent systems. Dr. Zhao’s academic excellence is also reflected in his ability to integrate theoretical knowledge with practical problem-solving, laying a strong foundation for his future research. His interdisciplinary approach blends pure mathematics with applied optimization techniques, making him uniquely positioned to contribute to emerging challenges in computational intelligence, machine learning, and navigation systems. His comprehensive training has equipped him with skills in advanced mathematical modeling, algorithm design, and statistical analysis—all crucial for his research trajectory.

Professional Experience

Dr. Shijie Zhao began his professional journey as a faculty member at Liaoning Technical University, where he is now serving as an Associate Professor and Director of the Institute of Intelligence Science and Optimization. Since 2012, he has progressed through a series of academic roles, including a postdoctoral tenure beginning in 2020. He has successfully led and participated in a range of scientific research projects sponsored by institutions such as the China Postdoctoral Science Foundation and the Department of Science & Technology of Liaoning Province. In addition to his teaching responsibilities, he has been actively involved in administrative, academic, and research leadership roles. Dr. Zhao has served as a reviewer for numerous high-impact international journals and conferences and has editorial roles in reputed scientific publications. His contributions to collaborative and interdisciplinary projects underscore his ability to bridge research and real-world applications, enhancing his standing as a key contributor in intelligent systems research.

Research Interest

Assoc. Prof. Dr. Shijie Zhao’s research interests lie at the intersection of intelligent optimization, computational mathematics, and advanced data analytics. He specializes in the development and enhancement of metaheuristic and multi-objective optimization algorithms, addressing both theoretical and application-driven challenges. His work has pioneered novel strategies for high-dimensional feature selection and optimization in machine learning contexts. Another key area of his focus is underwater navigation and positioning, where he has introduced innovative models for enhancing gravity navigation accuracy. With a strong foundation in mathematics, Dr. Zhao combines theoretical rigor with practical applicability, ensuring that his research contributes both to academic knowledge and technological development. His recent work explores how optimization strategies can be integrated into real-time systems, with implications in robotics, autonomous navigation, and engineering design. By addressing complex computational problems, Dr. Zhao’s research plays a vital role in driving forward the capabilities of intelligent systems and adaptive algorithms.

Award and Honor

Dr. Shijie Zhao has earned multiple accolades in recognition of his impactful contributions to scientific research and innovation. He has received four prestigious research awards for his work in intelligent systems, mathematical optimization, and applied computational modeling. His leadership in various national and provincial research initiatives has further cemented his reputation as a top-tier researcher in his domain. In addition to these honors, he has held editorial and reviewer positions for over ten internationally recognized journals, including publications by IEEE, Springer, and Elsevier—an acknowledgment of his expertise and academic integrity. Dr. Zhao is also an active member of 13 professional bodies, reflecting his global engagement and scholarly influence. His participation in high-impact collaborative projects and his growing citation index underscore the recognition and respect he commands in the research community. These honors validate his innovative spirit and unwavering dedication to advancing knowledge in mathematics and intelligent computing.

Conclusion

In conclusion, Assoc. Prof. Dr. Shijie Zhao exemplifies excellence in mathematical research, optimization theory, and intelligent system applications. His educational background, combined with over a decade of professional experience, positions him as a thought leader in his field. Through pioneering contributions to metaheuristic algorithms, multi-objective optimization, and underwater navigation, he bridges the gap between theoretical frameworks and practical technologies. His commitment to research integrity, academic service, and innovation has earned him widespread recognition and professional accolades. As an educator, leader, and scientist, Dr. Zhao’s multifaceted contributions reflect a deep dedication to advancing scientific knowledge and solving complex global challenges. His future endeavors are poised to have even greater impacts on the fields of artificial intelligence, data-driven decision-making, and intelligent navigation. With a strong publication record, a solid foundation in mathematics, and an expanding research network, Dr. Zhao continues to be a prominent and influential figure in the global academic landscape.

Publications Top Notes

  • Title: ID2TM: A Novel Iterative Double-Cross Domain-Center Transfer-Matching Method for Underwater Gravity-Aided Navigation
    Authors: Shijie Zhao, Zhiyuan Dou, Huizhong Zhu, Wei Zheng, Yifan Shen
    Year: 2025
    Source: IEEE Internet of Things Journal

  • Title: OS-BiTP: Objective sorting-informed bidomain-information transfer prediction for dynamic multiobjective optimization
    Authors: Shijie Zhao, Tianran Zhang, Lei Zhang, Jinling Song
    Year: 2025
    Source: Swarm and Evolutionary Computation

  • Title: Mirage search optimization: Application to path planning and engineering design problems
    Authors: Jiahao He, Shijie Zhao, Jiayi Ding, Yiming Wang
    Year: 2025
    Source: Advances in Engineering Software

  • Title: Twin-population Multiple Knowledge-guided Transfer Prediction Framework for Evolutionary Dynamic Multi-Objective Optimization
    Authors: Shijie Zhao, Tianran Zhang, Miao Chen, Lei Zhang
    Year: 2025
    Source: Applied Soft Computing

  • Title: VC-TpMO: V-dominance and staged dynamic collaboration mechanism based on two-population for multi- and many-objective optimization algorithm
    Authors: Shijie Zhao, Shilin Ma, Tianran Zhang, Miao Chen
    Year: 2025
    Source: Expert Systems with Applications

  • Title: A Novel Cross-Line Adaptive Domain Matching Algorithm for Underwater Gravity Aided Navigation
    Authors: Shijie Zhao, Wei Zheng, Zhaowei Li, Huizhong Zhu, Aigong Xu
    Year: 2024
    Source: IEEE Geoscience and Remote Sensing Letters

  • Title: Triangulation topology aggregation optimizer: A novel mathematics-based meta-heuristic algorithm for continuous optimization and engineering applications
    Authors: Shijie Zhao, Tianran Zhang, Liang Cai, Ronghua Yang
    Year: 2024
    Source: Expert Systems with Applications

  • Title: Improving Matching Efficiency and Out-of-Domain Positioning Reliability of Underwater Gravity Matching Navigation Based on a Novel Domain-Center Adaptive-Transfer Matching Method
    Authors: Shijie Zhao, Wei Zheng, Zhaowei Li, Huizhong Zhu, Aigong Xu
    Year: 2023
    Source: IEEE Transactions on Instrumentation and Measurement

  • Title: A dynamic support ratio of selected feature-based information for feature selection
    Authors: Shijie Zhao, Mengchen Wang, Shilin Ma, Qianqian Cui
    Year: 2023
    Source: Engineering Applications of Artificial Intelligence

  • Title: Sea-horse optimizer: a novel nature-inspired meta-heuristic for global optimization problems
    Authors: Shijie Zhao, Tianran Zhang, Shilin Ma, Mengchen Wang
    Year: 2023
    Source: Applied Intelligence

  • Title: Improving the Out-of-Domain Matching Reliability and Positioning Accuracy of Underwater Gravity Matching Navigation Based on a Novel Cyclic Boundary Semisquare-Domain Researching Method
    Authors: Shijie Zhao, Wei Zheng, Zhaowei Li, Huizhong Zhu, Aigong Xu
    Year: 2023
    Source: IEEE Sensors Journal

  • Title: A feature selection method via relevant-redundant weight
    Authors: Shijie Zhao, Mengchen Wang, Shilin Ma, Qianqian Cui
    Year: 2022
    Source: Expert Systems with Applications

  • Title: Dandelion Optimizer: A nature-inspired metaheuristic algorithm for engineering applications
    Authors: Shijie Zhao, Tianran Zhang, Shilin Ma, Miao Chen
    Year: 2022
    Source: Engineering Applications of Artificial Intelligence

  • Title: Improving Matching Efficiency and Out-of-domain Reliability of Underwater Gravity Matching Navigation Based on a Novel Soft-margin Local Semicircular-domain Re-searching Model
    Authors: Shijie Zhao, Wei Zheng, Zhaowei Li, Huizhong Zhu, Aigong Xu
    Year: 2022
    Source: Remote Sensing

  • Title: Improving Matching Accuracy of Underwater Gravity Matching Navigation Based on Iterative Optimal Annulus Point Method with a Novel Grid Topology
    Authors: Shijie Zhao, Wei Zheng, Zhaowei Li, Aigong Xu, Huizhong Zhu
    Year: 2021
    Source: Remote Sensing

  • Title: A Novel Quantum Entanglement‐Inspired Meta‐heuristic Framework for Solving Multimodal Optimization Problems
    Authors: Shijie Zhao
    Year: 2021
    Source: Chinese Journal of Electronics

  • Title: A Novel Modified Tree‐Seed Algorithm for High‐Dimensional Optimization Problems
    Authors: Shijie Zhao
    Year: 2020
    Source: Chinese Journal of Electronics

 

Farshid Dehghan | Optimization | Best Researcher Award

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Dr. Farshid Dehghan | Optimization | Best Researcher Award

Doctoral Researcher at Universidad Politécnica de Madrid, Iran

Farshid Dehghan is a dedicated Building Energy Performance Analyst with expertise in simulation-based optimization, energy efficiency, and machine learning applications. He is affiliated with Escuela Técnica Superior de Edificación, Universidad Politécnica de Madrid, Spain, where he focuses on sustainable building solutions. His research includes optimizing building retrofits in Iran to improve energy consumption, emissions reduction, comfort, and indoor air quality in the face of climate change. He is currently working on predicting energy consumption and emissions using machine learning approaches, reflecting his innovative mindset in data-driven sustainability. His scholarly contributions include a publication in the Sustainability journal, showcasing his ability to address real-world energy challenges. While his research impact is growing, expanding his indexed publications, securing patents, and increasing industry collaborations could further enhance his profile. With his commitment to sustainable energy solutions, Farshid Dehghan is a promising researcher in the field of building energy performance and smart optimization techniques.

Professional Profile 

Google Scholar

Education

Farshid Dehghan is affiliated with Escuela Técnica Superior de Edificación, Universidad Politécnica de Madrid, Spain, where he has built a strong academic foundation in building energy performance, sustainable design, and simulation-based optimization. His educational background is deeply rooted in engineering and environmental sustainability, equipping him with the necessary skills to tackle challenges related to energy efficiency, emissions control, and indoor air quality. His studies have provided him with expertise in machine learning applications for energy prediction and optimization, making him a forward-thinking researcher in the field. Throughout his academic journey, he has developed a strong analytical approach and a problem-solving mindset, allowing him to apply innovative methodologies to complex building energy problems. His educational background has played a crucial role in shaping his research focus, emphasizing the intersection of technology, energy efficiency, and sustainability, which forms the core of his work in simulation-based multi-objective optimization.

Professional Experience

Farshid Dehghan is a Building Energy Performance Analyst with expertise in sustainable building solutions, energy efficiency modeling, and simulation-based optimization techniques. His professional experience includes research on building retrofits in Iran, where he focuses on optimizing energy consumption, minimizing emissions, and improving occupant comfort while considering climate change impacts. His work integrates machine learning and data-driven approaches to predict energy consumption and emissions, demonstrating his strong analytical and computational skills. Through his research, he has gained experience in working with building simulation software, optimization tools, and statistical modeling techniques. His role requires him to analyze real-world building performance, propose effective retrofit solutions, and contribute to the advancement of energy-efficient building designs. Additionally, his work in academic publishing and industry-related consultancy projects has enabled him to apply his research to practical applications, making him a valuable asset in the field of sustainable building energy performance.

Research Interest

Farshid Dehghan’s research primarily focuses on building energy performance, simulation-based optimization, and machine learning applications in sustainability. He is particularly interested in multi-objective optimization for energy-efficient building retrofits, aiming to reduce energy consumption, minimize emissions, and enhance indoor air quality while ensuring occupant comfort. His work extends to predictive modeling using machine learning techniques, where he applies advanced algorithms to forecast energy usage patterns and environmental impacts. Additionally, he is exploring the integration of smart building technologies to develop data-driven strategies for optimizing building operations. His research aligns with global efforts to combat climate change by promoting energy-efficient and low-carbon building solutions. He is also interested in developing policy-driven strategies for sustainable urban environments, collaborating with experts across disciplines to create innovative frameworks for energy management and optimization. His research contributions reflect his commitment to sustainability and technological innovation in the built environment.

Awards and Honors

Farshid Dehghan’s contributions to building energy performance research have positioned him as a promising researcher in his field. While he is in the early stages of his career, his publication in the Sustainability journal and ongoing research projects demonstrate his growing impact. His work in simulation-based optimization for building retrofits has gained recognition, and as he continues to expand his research, he is likely to attract more academic and industry accolades. By securing indexed journal publications, patents, and industry collaborations, he has the potential to achieve prestigious honors in sustainable building research. His dedication to improving energy efficiency and indoor air quality aligns with global sustainability goals, making him a strong candidate for future research awards. As he continues to contribute to innovative energy solutions, his work is expected to receive further recognition in academic, industry, and policy-making circles.

Conclusion

Farshid Dehghan is a dedicated researcher and analyst specializing in building energy performance, sustainable design, and machine learning-driven energy optimization. His work addresses critical challenges in energy efficiency, emissions reduction, and occupant comfort, making significant contributions to the field of sustainable built environments. While his research is gaining traction, further expansion in indexed journal publications, patents, and industry partnerships will strengthen his profile. His expertise in simulation-based optimization and predictive modeling demonstrates his forward-thinking approach to sustainability. As he continues his research, his contributions will play a vital role in shaping the future of energy-efficient building solutions. His strong technical background, research-driven mindset, and commitment to innovation make him a valuable asset in the pursuit of sustainable and climate-resilient building technologies.

Publications Top Noted

 

Zohaib Khan | Optimization | Best Researcher Award

Published on by Math Scientist

Dr. Zohaib Khan | Optimization | Best Researcher Award

Jiangsu University, China

Zohaib Khan is a dedicated researcher specializing in machine learning, object detection, and control science engineering, with a strong focus on precision agriculture and AI-driven automation. Currently pursuing a PhD at Jiangsu University, China, he has made significant contributions to deep learning-based agricultural robotics, publishing multiple first-author papers in high-impact SCI Q1, Q2, and EI journals. His work emphasizes real-time detection, optimization algorithms, and AI-driven sustainability solutions. With extensive mentoring experience (50+ Bachelor’s and 10 Master’s students), he has played a key role in academic development. Zohaib has received numerous national and international awards, including first prizes in elite research and innovation competitions. His technical expertise spans Python, MATLAB, LaTeX, and AI-driven modeling, complementing his ability to lead interdisciplinary research. With a passion for advancing AI applications in agriculture, he continues to drive innovation in sustainable and automated farming solutions.

Professional Profile 

Google Scholar
Scopus Profile
ORCID Profile

Education

Zohaib Khan is currently pursuing a PhD in Control Science Engineering at Jiangsu University, China (2022–2026), specializing in machine learning and object detection. He previously earned an MSc in Electrical Engineering (2019–2022) from the same institution, focusing on power systems and renewable energy. His Bachelor’s degree in Electrical Power Engineering (2013–2017) from Swedish College of Engineering and Technology, Pakistan, laid the foundation for his technical expertise. His early academic years were marked by excellence, having completed Pre-Engineering at Fazaia Degree College (2011–2013) and his Secondary School Certificate (2009–2011) at Agricultural University Public School. Zohaib’s academic journey is distinguished by his strong analytical skills and passion for integrating AI and automation in engineering solutions. His education reflects a deep commitment to advanced research, innovation, and interdisciplinary problem-solving, positioning him as a future leader in AI-driven technologies and precision agriculture.

Professional Experience

Zohaib Khan has gained substantial experience in both academic research and engineering practice. As an intern at WAPDA, Pakistan, he developed hands-on expertise in power distribution and transmission lines, strengthening his understanding of grid operations and maintenance. Later, as an Electrical Engineer at LIMAK (JV) ZKB – CPEC Project (2017–2018), he contributed to electrical system design, installation, and maintenance, gaining valuable project management experience. His role involved troubleshooting, safety compliance, and interdisciplinary collaboration, enhancing his problem-solving capabilities. In academia, Zohaib has mentored over 50 Bachelor’s and 10 Master’s students, guiding them through research projects in machine learning, object detection, and automation. His strong writing, teaching, and IT skills have been instrumental in fostering innovation. His diverse experience, spanning applied research and engineering implementation, makes him a well-rounded professional capable of driving breakthroughs in AI-powered automation and precision agriculture.

Research Interest

Zohaib Khan’s research focuses on machine learning, deep learning, object detection, and AI-driven automation, with applications in precision agriculture and robotics. His studies revolve around real-time detection, optimization algorithms, and advanced control systems for agricultural sustainability and industrial automation. He has pioneered AI-driven precision farming techniques, developing deep learning-enhanced YOLOv7 and YOLOv8 algorithms for real-time crop health assessment and robotic spraying systems. Additionally, his work explores autonomous navigation in unstructured farmlands, energy-efficient control systems, and reinforcement learning for AI-based decision-making. His research extends to risk assessment in renewable energy systems, contributing to more efficient and resilient smart grids. Through interdisciplinary collaborations, Zohaib continues to push the boundaries of AI in sustainable agriculture, robotics, and industrial automation, aiming to develop intelligent, scalable, and high-impact solutions for modern technological challenges.

Awards and Honors

Zohaib Khan has received multiple prestigious awards recognizing his contributions to research, innovation, and academic excellence. He has won First Prizes in National Competitions, including the China University Business Elite Challenge (2024) and the Brand Planning Competition (2024). His research excellence was acknowledged with the Excellent Paper Award at the Sino-award (2021) and special recognition in Jiangsu Province Graduate Energy-saving and Low-Carbon Research Competition (2023). Additionally, he was honored as an Outstanding Student in the 17th “Yale School of Jiangsu University” program and received a Certificate of Excellence for Teaching Assistance. His leadership and public speaking skills earned him first place in an English debate at Jiangsu University. These accolades reflect his dedication to research, leadership in innovation, and commitment to advancing AI applications in engineering and agriculture, solidifying his reputation as a promising researcher in his field.

Conclusion

Zohaib Khan’s academic, professional, and research journey showcases his exceptional talent in AI-driven automation, machine learning, and precision agriculture. His extensive experience in research, mentoring, and engineering practice positions him as a leading scholar in intelligent agricultural robotics and sustainable AI applications. With a strong publication record in high-impact journals (SCI Q1, Q2, and EI) and multiple national and international awards, he has demonstrated his ability to drive innovation and solve real-world problems. His work in deep learning-based automation and AI-driven optimization techniques continues to push the boundaries of technology for sustainability and efficiency. As he progresses in his career, Zohaib remains committed to advancing cutting-edge research, fostering academic collaborations, and contributing transformative solutions in AI, robotics, and smart energy systems. His dedication and achievements make him a strong candidate for prestigious research awards and a key contributor to the future of AI in engineering and agriculture.

Publications Top Noted