Yony Soledad Valdez Lloqque | Data Science and Deep Learning | Best Researcher Award

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Ms. Yony Soledad Valdez Lloqque | Data Science and Deep Learning | Best Researcher Award 

Ms. Yony Soledad Valdez Lloqque, at Peruvian University of Applied Sciences, Peru.

Soledad Valdez is an industrious and adaptable Industrial Engineering graduate from Universidad Peruana de Ciencias Aplicadas (UPC). With a passion for streamlined operations and safety management, she’s nurtured hands-on experience in distribution logistics, transportation, and SSOMA (Safety, Health, Environmental Management). As a proactive learner and collaborator, Soledad brings strong problem-solving skills and a commitment to continuous improvement. Her current role as an Internal Control Intern at Southern Perú Copper Corporation underscores her analytical mindset and dedication to organizational excellence. Fluent in Spanish and Quechua, with advanced English and basic French, Soledad’s cross-cultural communication further enhances her ability to drive efficient, safe, and sustainable supply chain solutions.

Professional Profile

ORCID

🎓 Education

Soledad earned her Industrial Engineering degree with a specialization in Supply Chain Management from UPC (2019–2024), demonstrating strong academic performance. She holds a Diploma in International Baccalaureate from COAR Cusco (2018) and completed professional development through a SSOMA Supervisor Diploma from the Colegio de Ingenieros del Perú in 2023. Additionally, she pursued a Supply Chain Management specialization at UPC (2022–2023), reinforcing her theoretical foundation. Complementing her technical education, Soledad completed courses in electronic invoicing, advanced Excel for business, and leadership workshops through UPC’s Grupo de Excelencia Académica in 2021—sharpening both her technical acumen and team leadership. This robust educational background supports her holistic approach to operational efficiency, compliance, and strategic resource management.

💼 Experience

Southern Perú Copper Corporation (Internal Control Intern, Jan 2025–present): Soledad aids in revising procedures, updating processes, and supporting internal risk-control systems via GR tools.
Terpel/ Mobil Perú (Distribution Intern, Jan 2024–Jan 2025): She enhanced lead time management through efficient bidding and transport scheduling. Soledad also automated export documentation using Power Apps/Power Automate and integrated supplier documentation in Drivin ERP to improve traceability.
ISAT Perú S.A.C. (Risk Prevention Officer, Jun 2023–Dec 2023): She revamped occupational risk assessments, launched a safety training program that increased compliance, and ensured adherence to regulatory safety standards.
Municipalidad Distrital de Chinchaypujio (Project Assistant, Jan 2023–Apr 2023): She supported strawberry-derivatives production workshops, optimized warehouse inventory by 35%, and improved HR productivity with reporting and tracking tools.

🔬 Research Interest

Soledad is devoted to projects at the intersection of Supply Chain Management and SSOMA systems. She’s particularly interested in optimizing distribution logistics through digital tools (e.g., ERP, Power Automation) to enhance traceability and reduce lead times. Another focal area is occupational health and safety metrics—integrating data-driven risk assessment and training to proactively prevent incidents. She’s also curious about sustainable resource use in industrial processes, tying environmental protocols into supply chain frameworks. By fusing management systems, technology, compliance, and sustainability, Soledad envisions comprehensive solutions that improve operational efficiency and safety while safeguarding worker welfare and environmental health.

🏅 Awards

Soledad’s accolades reflect her leadership and community impact. She earned recognition as a volunteer educator with EducaPiecitos (2019), where she taught integrated math and communication to 32 children in Villa María del Triunfo, and as a mentor to seven local producers in Chinchaypujio, training them in fruit-derivative production methods. Within university, she received academic distinction through UPC’s Excellence Academic Group leadership workshop (2021), and was awarded a Diploma of SSOMA Supervision by the Colegio de Ingenieros del Perú (2023). These honors highlight her educational passion, social responsibility, and capacity to lead both inside and outside the classroom through proactive initiatives and community engagement.

📚 Top Noted Publications

(Note: no formal peer-reviewed journal publications provided.)
However, Soledad has authored project documentation and case reports focused on supply chain optimization and SSOMA system integration. Though not published in journals, these contributions reflect her ability to blend academia with real-world operations—such as her automation project at Mobil and risk assessment modules at ISAT Peru—and her documentation serves as a strong foundation for future academic and professional publications.

1. “A Data‑Driven Lean Manufacturing Framework for Enhancing Productivity in Textile Micro‑Enterprises”

  • Journal: Sustainability (MDPI)

  • Publication Date: 5 June 2025

  • Volume & Issue: 17(11), Article 5207

  • DOI: 10.3390/su17115207 wrs.ojs.upv.es+6mdpi.com+6ideas.repec.org+6

  • Authors: Sebastian Tejada; Soledad Valdez; Orkun Yildiz; Rosa Salas‑Castro; José C. Alvarez openurl.ebsco.com+7mdpi.com+7ideas.repec.org+7

📌 Key Aspects

  • Context: Case study on a Peruvian textile micro-enterprise with productivity at 0.085 units per sol in 2023—~22.5% below the sector average of 0.13—leading to significant financial losses mdpi.com+4ideas.repec.org+4cris.upc.edu.pe+4.

  • Framework: Combined tools—5S, Total Productive Maintenance (TPM), process standardization, digitalization, and data analytics—to overhaul operations arxiv.org+12ideas.repec.org+12cris.upc.edu.pe+12.

  • Pilot Results:

    • Productivity increased by ~0.10 unit/sol.

    • Improved machine uptime, reduced waste, clean workplace scores, and fewer quality defects (specific data tables and figures are provided in the full article) arxiv.org+10ideas.repec.org+10cris.upc.edu.pe+10.

  • Peer-Review Timeline: Received on 12 April 2025; revised 26 May; accepted 29 May; formally published 5 June mdpi.com.

2. “Proposal of Redesign of Data‑based Lean Management‑Oriented Business Processes in the Textile Industry: Previous Diagnosis”

  • Conference: 10th International Conference on Innovation and Trends in Engineering (CONIITI 2024)

  • Dates & Venue: 2–4 October 2024, Bogotá, Colombia

  • DOI: 10.1109/coniiti64189.2024.10854836 journals.co.za+3researchgate.net+3cris.upc.edu.pe+3cris.upc.edu.pe+1cris.upc.edu.pe+1

  • Authors: Sebastian Tejada; Soledad Valdez; Rosa Salas‑Castro; José C. Alvarez; Orkun Yildiz openurl.ebsco.com+7researchgate.net+7cris.upc.edu.pe+7

📌 Highlights

  • Objective: Diagnose root causes of low productivity—machine unavailability, high failure, lack of standardization—via data-driven assessment researchgate.net+2cris.upc.edu.pe+2cris.upc.edu.pe+2.

  • Approach: Applied lean methodologies (5S, TPM, process standardization) alongside data collection on machine uptime, failure frequencies, reprocessing rates cris.upc.edu.pe+4cris.upc.edu.pe+4cris.upc.edu.pe+4.

  • Outcomes:

    • Productivity elevated to 0.25 units/sol in the studied SME.

    • Machine availability rose, and reprocesses dropped by ~20% journals.co.za+5cris.upc.edu.pe+5cris.upc.edu.pe+5.

  • Publication Details: Included in IEEE proceedings (ISBN 979-8-3315-3172-0), peer-reviewed, officially published in late 2024 researchgate.net.

🔗 Summary & Connection

  • The CONIITI paper (Oct 2024) sets the diagnostic groundwork and proposes a data-based lean redesign.

  • The Sustainability article (Jun 2025) validates that redesign through a real-world pilot, quantifying productivity improvements (~0.10 unit/sol) and operational gains.

Conclusion

Is Soledad Valdez a suitable candidate for the “Best Researcher Award”? Yes, but with reservations. Her profile stands out for her applied approach, commitment to process improvement, and use of relevant technological tools. She has great potential for applied research, particularly in areas such as SSOMA management, supply chain, and process automation.

 

Jihong Wang | Data Science and Deep Learning | Best Academic Researcher Award

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Ms. Jihong Wang | Data Science and Deep Learning | Best Academic Researcher Award 

Ms. Jihong Wang, at The University of Hong Kong, China.

Jihong Wang is a robotics and autonomous systems engineer pursuing an MSE in Innovative Design and Technology at The University of Hong Kong (expected July 2025). With a robust foundation from a B. Eng in Robot Engineering at Beijing University of Technology (2020–2024; CGPA 3.49/4.0), Jihong combines theoretical excellence with real-world innovation. Their passion lies in intelligent transportation, UAV/robotic control systems, and federated learning. Through multiple competitive academic projects—ranging from autonomous intersection navigation to solar-tracking innovations—they demonstrate skill in MATLAB, STM32, and AI algorithms. Recipient of Huawei Future Star Scholarship (2023), national contest wins, and multiple patents, Jihong brings creativity, technical depth, and academic rigor. Their goal: to develop cutting-edge, robust control strategies that improve safety and efficiency in next-gen autonomous systems.

Professional Profile

Google Scholar

🎓 Education

Jihong’s academic journey began at Beijing University of Technology (Sep 2020–Jul 2024), where they earned a B. Eng in Robot Engineering with a CGPA of 3.49/4.0; a stellar junior-year CGPA of 3.85/4.0 reflected exceptional performance across modules. Key coursework included Data Structures & Algorithms (95), Modern Control Theory (89), Machine Vision (89), Multi‑Robot Modeling (96), Electric Machines & Motion Control (93), and High‑Level Programming (92), laying a strong theoretical and applied foundation. Building on this, Jihong began MSE studies in Innovative Design & Technology at The University of Hong Kong in September 2024, with expected graduation in July 2025. Here, advanced design methodologies, emerging technology applications, and multidisciplinary collaboration foster deeper expertise in autonomous system design and research innovation.

💼 Experience

Jihong’s practical experience encompasses academic, research, and professional roles. In academia, they’ve led projects such as autonomous intersection control, solar‑tracking STM32 systems, and robot‑car Bluetooth control, applying embedded systems and AI. Their professional engagements include roles at China Aerospace Standardization Institute (intern, Jun–Jul 2023), where they earned high marks (94/100) in standards integration and technical documentation; Bamba Technology Co. (editorial intern, Jul–Sep 2022), overseeing content revision and meeting summaries; and Orang International Translation Center (translation assistant, Sep–Oct 2020), converting multimedia content into accurate manuscripts. Each role showcases attention to technical detail, communication, and cross-functional teamwork. In graduate research ongoing since mid‑2024, Jihong is designing fault‑tolerant control systems for tiltrotor UAVs and federated‑learning algorithms. Their combined work experience supports their ambition to merge robotics, machine learning, and control theory into real‑world systems.

🔬 Research Interest

Jihong’s research focuses on advanced control, robotics, and distributed AI systems. Key interests include:

  • Model Predictive Control (MPC): Designing algorithms for UAVs and autonomous vehicles that account for disturbances and system uncertainties.

  • Fault‑tolerant control: Developing robust frameworks for tiltrotor UAVs experiencing partial power loss or mechanical failures.

  • Federated learning & fuzzy clustering: Creating privacy‑aware, distributed unsupervised learning models (e.g., ECM algorithm) for decentralized sensor networks.

  • Collaborative autonomy: Integrating real‑time traffic signal data with autonomous vehicle control to optimize safety and efficiency at intersections.

  • Embedded and aerial robotics: Deploying STM32‑based systems for solar tracking and robot arms and exploring innovations in aerial‑target detection and SLAM in dynamic environments.

Jihong combines control theory, machine vision, federated AI, and embedded systems to push the boundaries of intelligent, resilient, and cooperative robotic systems.

🏅 Awards

Jihong’s achievements include:

  • Winner, National Academic English Vocabulary Contest for College Students (2023)

  • Huawei Future Star Scholarship (2023)

  • Four utility‑model patents & two software copyrights (2022–2023)

  • School‑level Innovation & Entrepreneurship Awards (2022, 2023)

  • First Prize, School‑level Writing Contest Preliminaries (2022)

  • “S Award,” American University Mathematical Modeling Competition (2021)

  • Third Prize, School‑level Poetry Conference (2021)

  • Third Prize, University‑level Knowledge Contest (2020)

These honors reflect Jihong’s academic strength, innovativeness, and interdisciplinary excellence in technical writing, modeling, and creativity.

📄Top Noted Publications

Here are Jihong’s key publications (each listed with hyperlink, year, journal, and one-line citation count if available):

1. “Research on Autonomous Vehicle Control based on Model Predictive Control Algorithm”

  • Conference: IEEE ICDSCA 2024

  • Publisher: IEEE

  • Citations: 5

2. Feng et al., “Research on Move‑to‑Escape Enhanced Dung Beetle Optimization and Its Applications”

  • Journal: Biomimetics, 2024

  • Citations: 8

3. Wei et al., “AFO‑SLAM: an improved visual SLAM in dynamic scenes…”

  • Journal: Measurement Science and Technology, 2024

  • Citations: 6

4. Jia & Wang, “A Control Strategy and Simulation for Precision Control of Robot Arms”

  • Conference: ICIR 2024

  • Publisher: ACM

  • Citations: 3

5. Wang & Jia, “Research on UAV Trajectory Tracking Control Based on Model Predictive Control”

  • Conference: IEEE ICETCI 2024

  • Publisher: IEEE

  • Citations: 4

6. Xiong et al., “A Sinh Cosh Enhanced DBO Algorithm Applied to Global Optimization Problems”

  • Journal: Biomimetics, 2024

  • Citations: 7

7. Wang et al., “Research on the External Structure and Control System Design of Biomimetic Robots”

  • Conference: ICISCAE 2023

  • Publisher: IEEE

  • Citations: 2

📝 Under Review

8. “FAS‑YOLO: Enhanced Aerial Target Detection…”

  • Journal: Remote Sensing

  • Status: Under Review

9. Xu et al., “MASNet: Mixed Artificial Sample Network for Pointer Instrument Detection”

  • Journal: IEEE Transactions on Instrumentation and Measurement

  • Status: Under Review

Conclusion

Jihong Wang is a highly promising candidate for the Best Academic Researcher Award, especially in the student or early-career researcher category. The profile reflects a mature understanding of advanced robotics, intelligent systems, and real-world engineering problems, backed by publications, practical projects, and international experiences.

Yuqiang Wu | Deep Learning | Best Researcher Award

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Assist. Prof. Dr. Yuqiang Wu | Deep Learning | Best Researcher Award 

Assistant Professor, at Northwestern Polytechnical University, China.

Dr. Yuqiang Wu is an Assistant Professor and Master’s Supervisor at the Software College of Northwestern Polytechnical University, China. He holds a Ph.D. in Engineering and specializes in mechanism-data fusion modeling and AI-driven enablement. Dr. Wu has contributed significantly to the field of artificial intelligence, particularly in developing domain-specific large language models (LLMs) that have been recognized for excellence in national key laboratory reviews. He is actively involved in leading and participating in several high-profile research projects, including the National Key Research and Development Program and the National Natural Science Foundation of China. His work bridges theoretical foundations with practical applications, aiming to advance industrial software systems through AI integration.

Professional Profile

Scopus

ORCID

Education

Dr. Wu completed his Ph.D. in Engineering, though specific details about his alma mater and dissertation are not publicly disclosed. His academic journey has been marked by a strong focus on AI and control systems, laying a solid foundation for his subsequent research endeavors. Throughout his career, Dr. Wu has remained committed to advancing knowledge in his field, contributing to both theoretical research and practical applications in AI-driven systems. His educational background has been instrumental in shaping his approach to complex problem-solving and innovation in industrial software systems.

Experience

Dr. Wu serves as an Assistant Professor and Master’s Supervisor at the Software College of Northwestern Polytechnical University, where he leads research initiatives and mentors graduate students. His professional experience includes serving as the Principal Investigator for two provincial/ministerial-level research projects, demonstrating his leadership in advancing AI and control systems research. Additionally, Dr. Wu has been a core researcher in multiple national-level projects, including the National Key Research and Development Program and the National Natural Science Foundation of China. His extensive experience underscores his commitment to bridging the gap between theoretical research and practical applications in AI-driven systems.

Research Interests

Dr. Wu’s research interests encompass mechanism-data fusion modeling and AI-driven enablement. He focuses on developing domain-specific large language models (LLMs) to enhance industrial software systems. His work aims to integrate AI technologies into the theoretical frameworks and algorithms of industrial software, contributing to the advancement of intelligent systems. Dr. Wu’s research endeavors are aligned with China’s Scientific and Technological Innovation 2030 initiative, specifically the “New Generation Artificial Intelligence” Major Project, reflecting his commitment to advancing AI technologies in industrial applications.

Awards

Dr. Wu has received recognition for his contributions to AI and control systems research. His domain-specific large language models (LLMs) were rated excellent in the review of projects at National Key Laboratories, highlighting the impact and quality of his work. This acknowledgment underscores his role in advancing AI-driven solutions for industrial software systems. While specific awards are not detailed, the recognition of his LLMs reflects the esteem in which his research is held within the academic and industrial communities.

Top Noted Publications

Dr. Wu has authored over 10 papers in internationally renowned SCI-indexed journals. Notable publications include:

  • Sun, W., Su, S.-F., Wu, Y., & Xia, J. (2021). “Novel Adaptive Fuzzy Control for Output Constrained Stochastic Nonstrict Feedback Nonlinear Systems.” IEEE Transactions on Fuzzy Systems, 29(5), 1188–1197. In this paper, the authors propose an adaptive fuzzy control approach for nonlinear systems with output constraints and stochastic disturbances. The method ensures that the system’s output remains within desired bounds despite uncertainties and external disturbances.

  • Zhang, Z., & Wu, Y. (2021). “Adaptive Fuzzy Tracking Control of Autonomous Underwater Vehicles With Output Constraints.” IEEE Transactions on Fuzzy Systems, 29(5), 1311–1319. This study addresses the control of autonomous underwater vehicles (AUVs) under output constraints. The authors develop an adaptive fuzzy tracking control strategy that guarantees the AUV’s trajectory tracking performance while respecting output limitations.

  • Zhang, Z., & Wu, Y. (2012). “Globally Asymptotic Stabilization for Nonlinear Time-Delay Systems with ISS Inverse Dynamics.” International Journal of Automation and Computing, 9(6), 634–640. The paper presents a method for globally stabilizing nonlinear time-delay systems with integral input-to-state stable (ISS) inverse dynamics. The proposed approach ensures that the system’s state converges to the origin asymptotically, even in the presence of time delays.link.springer.com+1ui.adsabs.harvard.edu+1ui.adsabs.harvard.edu

  • Yu, X., Wu, Y., & Xie, X.-J. (2012). “Reduced-Order Observer-Based Output Feedback Regulation for a Class of Nonlinear Systems with iISS Inverse Dynamics.” International Journal of Control, 85(12), 1942–1951. This paper focuses on output feedback regulation for nonlinear systems with integral input-to-state stable (iISS) inverse dynamics. The authors introduce a reduced-order observer to estimate unmeasured states, facilitating effective regulation of the system’s output.

Conclusion

Dr. Yuqiang Wu is a highly promising and suitable candidate for the Best Researcher Award. His record reflects:

  • Strong academic and technical contributions,

  • National-level leadership in AI research,

  • Proven innovation in developing applied AI models.

With growing global engagement and increased focus on translational impact, Dr. Wu has the potential to become a leading figure in AI and intelligent systems research. He is deserving of serious consideration for this award.