Aaron Finley | Business Intelligence | Research Excellence Award-duplicate-1

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Aaron Finley | Business Intelligence | Research Excellence Award

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Dr. Aaron Finley | Business Intelligence | Research Excellence Award

Assistant Professor at Macau University of Science and Technology | Macau

Dr. Aaron Finley is a researcher at the Macau University of Science and Technology, Macau, with expertise in Business Intelligence, data-driven policy analysis, and applied econometric modeling. His research focuses on the intersection of sustainability analytics, environmental economics, public health modeling, and advanced statistical methodologies. Dr. Finley has made significant scholarly contributions in evaluating carbon pricing instruments and their effectiveness in reducing emissions across major Asian economies, providing evidence-based insights for climate policy optimization. His work on environmental, social, and governance (ESG) factors in relation to business environments demonstrates the practical application of multivariate analysis techniques such as canonical correlation analysis in regional economic systems.

In addition to sustainability and economic modeling, Dr. Finley’s interdisciplinary research extends into public health analytics, where he applies predictive modeling, diffusion theory, and cost-effectiveness analysis to pandemic response strategies, vaccination behaviors, and lung cancer screening programs in Asia. His studies published in BMC Medicine, Journal of Thoracic Disease, Sustainable Futures, and Sustainability highlight his ability to translate complex data into actionable policy insights. Through the integration of business intelligence frameworks with health and environmental datasets, Dr. Finley’s research supports informed decision-making in government, healthcare, and sustainability-focused institutions. His growing citation impact reflects the relevance and applicability of his work across multiple high-impact domains.

Profiles: Scopus | Google Scholar

Featured Publications

  • Finley, A., He, W., Huang, H., & Hon, C. (2024). Analyzing the effectiveness of carbon pricing instruments in reducing carbon emissions in major Asian economies. Sustainability, 16(23), 10542.
    Citation Count: 5

  • Finley, A., He, W., Huang, H., & Hon, C. (2025). A canonical correlation analysis on the relation of environmental, social, governance (ESG) on business environment (paying taxes) in South China. Sustainable Futures, 10, 101369.

  • Zhang, X., Shi, W., Liu, Z., Finley, A., Cen, K., Xie, Z., Yang, P., Li, H., & Leong, U. (2025). Adaptive Fourier decomposition analysis of different pandemic stages in South Korean cities: Policies and trends. Journal of Thoracic Disease, 17(6), 3516–3531.

  • Zhang, T., Wang, Y., Chen, X., Yang, X., Zhang, L., Bazzi, N., Bai, L., & Finley, A. (2025). Cost-effectiveness of risk model-based lung cancer screening in smokers and nonsmokers in China. BMC Medicine, 23(1), 315.

  • He, W., Wu, J., Chen, C. H., Finley, A., Wang, H., Huang, H., Ng, C., & Chui, T. (2025). Predicting COVID-19 vaccination timing by integrating the theory of planned behavior and the diffusion of innovations: A cross-sectional survey in Macao, China. Journal of Thoracic Disease, 17(5), 2813.

Danheng Gao | Deep Learning | Research Excellence Award

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Prof. Dr. Danheng Gao | Deep Learning | Research Excellence Award

Associate Researcher at Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences | China

Prof. Dr. Danheng Gao is a distinguished researcher at the Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, China, specializing in deep learning and its integration with advanced optical and photonic systems. His research bridges the disciplines of machine learning, surface-enhanced Raman spectroscopy (SERS), nonlinear optics, and ultrafast photonics, with a strong emphasis on intelligent data-driven strategies for real-world analytical applications. Prof. Gao has made notable contributions to the development of rapid identification and sensing technologies by combining artificial intelligence with spectroscopic techniques, significantly enhancing accuracy, speed, and automation in chemical and food analysis. His work in ultrafast photonics further explores the convergence of nonlinear optical phenomena with intelligent control systems, enabling breakthroughs in high-speed optical signal processing and precision measurement. Through high-impact publications in leading journals such as Food Chemistry, his research demonstrates strong interdisciplinary value across photonics, artificial intelligence, and applied chemistry. With growing citation impact, Prof. Gao is recognized for advancing intelligent optical sensing, machine-learning-driven spectroscopy, and next-generation photonic technologies.

Profile: Scopus

Featured Publications

  1. Gao, D., et al. (2025). A rapid wine brand identification method based on the joint application of SERS and machine learning techniques.

  2. Gao, D., et al. (2025). Advancements in ultrafast photonics: Confluence of nonlinear optics and intelligent strategies.
    Citation Count: 6

Libo Zhou | Digital Implantology | Research Excellence Award

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Dr. Libo Zhou | Digital Implantology | Research Excellence Award

Associate Chief Physician at Jiamusi University Affiliated Stomatological Hospital | China

Dr. Libo Zhou is a highly respected specialist in digital implantology whose career reflects a strong commitment to advancing dental technology, clinical innovation, and interdisciplinary collaboration. Recognized as an influential figure in the integration of robotics within oral implantology, he has significantly contributed to the development, refinement, and clinical application of robotic surgical systems, demonstrating exceptional expertise in guiding complex digital workflows and precision-based implant procedures. His leadership in pioneering robotic full-arch implant interventions and expanding the use of dynamic navigation has played a vital role in elevating clinical standards and shaping the future of technologically enhanced dental care. As an academic leader and research mentor, he fosters a culture of scientific inquiry, guiding projects that explore advanced imaging techniques, digital planning methodologies, and innovations in automated surgical guidance. His research portfolio includes numerous peer-reviewed publications in reputable indexed journals, reflecting a strong focus on improving procedural accuracy, enhancing patient outcomes, and addressing key engineering challenges in medical-dental integration. He has also contributed to national and regional advancements through patents, collaborative multidisciplinary initiatives, and active participation in professional committees dedicated to implantology and medical-engineering innovation. His work has earned recognition from scientific communities for excellence in research and contributions to clinical practice, further establishing his influence in transforming traditional approaches to implant dentistry. With a steadfast vision for the future of digital healthcare, he continues to drive translational research, promote industry-academia partnerships, and support the evolution of intelligent surgical systems that bridge technological capability with clinical expertise. His professional journey reflects a dedication not only to personal advancement but also to elevating the standards of practice, education, and innovation within the broader field of oral implantology.

Profiles: Scopus | ORCID

Featured Publications

Zhao, W., Teng, W., Su, Y., & Zhou, L. (2024). Accuracy of dental implant surgery with freehand, static computer-aided, dynamic computer-aided, and robotic computer-aided implant systems: An in vitro study. The Journal of Prosthetic Dentistry.

Zhou, L., Wu, F., Wang, J., Zhao, Y., Wu, G., & Su, Y. (2024). Effects of endoplasmic reticulum stress on chondrocyte apoptosis via the PI3K/AKT signaling pathway. Tissue and Cell.

Zhou, L., Teng, W., Li, X., & Su, Y. (2023). Accuracy of an optical robotic computer-aided implant system and the trueness of virtual techniques for measuring robot accuracy evaluated with a coordinate measuring machine in vitro. The Journal of Prosthetic Dentistry.

Ma, R., Liu, Q., Zhou, L., & Wang, L. (2023). High porosity 3D printed titanium mesh allows better bone regeneration. BMC Oral Health.

Zhou, L., Chen, D., Liu, P., Chen, L., & Su, Y. (2022). miR-132-3p participates in the pathological mechanism of temporomandibular joint osteoarthritis by targeting PTEN. Archives of Oral Biology.

Peik Foong Yeap | Artificial Intelligence | Best Academic Researcher Award

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Dr. Peik Foong Yeap | Artificial Intelligence | Best Academic Researcher Award

Senior Lecturer at University of Newcastle | Singapore

Dr. Yeap Peik Foong is a distinguished academic and researcher whose career reflects a deep commitment to advancing knowledge in strategic management, organisational development, cross-cultural management, sustainability practices, and innovation within higher education and industry. Renowned for her interdisciplinary perspective, she has contributed extensively to scholarly literature through impactful journal articles, book chapters, and international conference presentations that explore themes such as digital transformation, human–AI collaboration, leadership effectiveness, consumer behaviour, knowledge management, environmental sustainability, and community-based tourism. Her work is recognized for its ability to merge theoretical frameworks with real-world applications, offering insights that guide policy development, organisational strategy, and educational leadership. She has played influential roles in shaping academic programs, strengthening research culture, and supporting curriculum innovation, while also contributing actively as a reviewer, editorial board member, and examiner for reputable journals, conferences, and institutions worldwide. Her research leadership is further demonstrated through her involvement in numerous funded projects that address emerging challenges in digital well-being, workplace resilience, global responsibility, cybersecurity, internationalisation of higher education, and interorganisational collaboration. Known for her mentorship and supervision of postgraduate candidates, she has supported research that spans management, marketing, organisational behaviour, and industry-specific strategic studies, helping shape future scholars and professionals. Her consistent engagement with global academic communities, coupled with her ability to foster collaborative networks, reflects her dedication to elevating research standards and promoting sustainable, innovative, and culturally aware practices across sectors. Dr. Yeap’s body of work positions her as a respected thought leader whose scholarly contributions and service continue to influence contemporary debates and future directions in management, education, and organisational sustainability.

Profile: Scopus

Featured Publications

Ha, H., Yeap, P. F., Loh, H. S., & Pidani, R. (2025). Environmental sustainability and CSR practices by banks in Indonesia, Malaysia, and Singapore.

Tan, K. L., Yeap, P. F., Cheong, K. C. K., & Shanu, R. (2025). Crafting an organizational strategy for the new era: A qualitative study of artificial intelligence transformation in a homegrown Singaporean hotel chain.

Tan, K.-L., Loganathan, S. R., Pidani, R. R., Yeap, P.-F., Ng, D. W. L., Chong, N. T. S., Liow, M. L. S., Cheong, K. C.-K., & Yeo, M. M. L. (2024). Embracing imperfections: A predictive analysis of factors alleviating adult leaders’ digital learning stress on Singapore’s lifelong learning journey.

Yeap, P. F., & Liow, M. L. S. (2023). Tourist walkability and sustainable community-based tourism: Conceptual framework and strategic model.

Ong, H. B., Chong, L. L., Choon, S. W., Tan, S. H., Yeap, P. F., & Kasuma, N. M. H. (2022). Retaining skilled workers through motivation: The Malaysian case.

Lee, Y. W., Dorasamy, M., Ahmad, A. A., Jambulingam, M., Yeap, P. F., & Harun, S. (2021). Synchronous online learning during movement control order in higher education institutions: A systematic review.

Kuai Zhou | Computer Vision | Young Researcher Award

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Dr. Kuai Zhou | Computer Vision | Young Researcher Award

Lecturer at School of Aeronautical Engineering | Nanjing University of Industry Technology | China

Kuai Zhou is an emerging researcher in advanced aerospace manufacturing whose work integrates computer vision, deep learning, robotic automation, and precision aircraft assembly, positioning him as a promising contributor to the evolution of intelligent manufacturing systems. With a strong academic foundation in aerospace manufacturing engineering, he has developed deep expertise in visual measurement, robotic manipulation, and metrology for complex assembly tasks, building a portfolio of impactful publications and patented innovations that highlight both technical rigor and forward-looking research ambition. His scholarly contributions span high-quality scientific journals, where he has advanced methods for monocular visual measurement, high-precision six-degree-of-freedom pose estimation, super-resolution-enhanced assembly accuracy, convolutional-neural-network-based calibration techniques, adaptive insertion strategies, and robust machine-vision algorithms designed for the precise alignment and assembly of intricate components. These works collectively contribute to overcoming long-standing challenges in accuracy, automation, and reliability within large-scale aircraft assembly environments. Beyond his academic achievements, he has played an important role in national research initiatives focused on aerospace innovation, contributing to technological development in areas requiring high-precision visual sensing, automated alignment, and intelligent robotic assistance. His research and patented solutions consistently emphasize the integration of theoretical modeling with practical engineering, enabling more efficient workflows, reducing human dependence in critical assembly processes, and strengthening the foundational technologies required for future aerospace manufacturing ecosystems. With recognized expertise in computer vision, robotics, automation, and image processing, he continues to push the boundaries of intelligent aircraft assembly, helping shape the next generation of smart manufacturing and autonomous industrial systems while establishing himself as a rising figure in the field of aerospace engineering.

Profile: Google Scholar

Featured Publications

Kong, S. H. J., Huang, X., & Zhou, K. (2023). Online measurement method for assembly pose of gear structure based on monocular vision. Measurement Science and Technology, 34(6), 065110.

Kong, S. H. J., Huang, X., Zhou, K., & Li, H. Y. (2021). Detection method of addendum circle of gear structure based on machine vision. Chinese Journal of Scientific Instrument, 42(4), 247–255.

Li, H., Huang, X., Chu, W., Zhou, K., & Zhao, Z. (2021). 一种面向齿形结构装配的视觉测量方法. Laser & Optoelectronics Progress, 58(16), 1610003.

Zhou, K., Huang, X., Li, S., Li, H., & Kong, S. (2021). 6-D pose estimation method for large gear structure assembly using monocular vision. Measurement, 183, 109854.

Zhou, K., Huang, X., Li, S., & Li, G. (2023). Convolutional neural network-based pose mapping estimation as an alternative to traditional hand–eye calibration. Review of Scientific Instruments, 94(6).

Zhou, K., Huang, X., Li, S., & Li, G. (2023). Improving pose estimation accuracy for large hole shaft structure assembly based on super-resolution. Review of Scientific Instruments, 94(6).

Eric Howard | Artificial Intelligence | Research Excellence Award

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Dr. Eric Howard | Artificial Intelligence | Research Excellence Award

Honorary Research Fellow at Macquarie University | Australia

Dr. Eric Howard is a distinguished multidisciplinary scholar whose contributions span quantum computing, artificial intelligence, data science, cybersecurity, theoretical physics, and scientific philosophy, recognized for advancing both foundational research and transformative technological innovation. His work integrates quantum information theory with machine learning, leading to pioneering developments in quantum-classical neural networks, AI-enhanced intrusion detection models, quantum Bayesian inference frameworks, and advanced simulation methods for exploring molecular systems and emergent physical phenomena. With expertise that bridges scientific rigor and applied innovation, he has contributed significantly to research on quantum graph neural networks, holographic beam shaping, variational algorithm design, and AI-driven optimization for next-generation computational systems. His scholarly output includes a substantial body of peer-reviewed publications across major scientific outlets, along with editorial leadership in physics and theoretical sciences, where he supports global research through special issues, journal editing, and peer-review responsibilities. As an author and thought leader, he has produced influential academic texts and continues to develop works that deepen the understanding of machine learning theory and the evolution of quantum scientific paradigms. His professional impact extends into industry through leadership roles in AI-enabled cybersecurity and digital intelligence ventures, translating advanced theoretical models into practical solutions for threat analytics, secure digital infrastructures, cloud intelligence, and automated decision systems. Actively involved in leading scientific societies across computing, optics, physics, mathematics, and interdisciplinary research, he contributes to knowledge communities that shape the future of computational science and emerging technologies. Across academia, research, and innovation ecosystems, he is recognized for his ability to unify quantum science, intelligent computation, and high-impact problem solving, establishing a reputation as an influential figure driving progress at the intersection of advanced physics, machine intelligence, and next-generation technological development.

Profile: Google Scholar

Featured Publications

Ackley, K., Adya, V. B., Bailes, M., Blair, D., Lasky, P., & Howard, E. (2020). Neutron Star Extreme Matter Observatory: A kilohertz-band gravitational-wave detector in the global network.

Xue, X., Bian, L., Shu, J., Yuan, Q., Zhu, X., Bhat, N. D. R., Dai, S., Feng, Y., … (2021). Constraining cosmological phase transitions with the Parkes pulsar timing array.

Yoshiura, S., Pindor, B., Line, J. L. B., Barry, N., Trott, C. M., Beardsley, A., … (2021). A new MWA limit on the 21 cm power spectrum at redshifts ∼13–17.

Xue, X., Xia, Z. Q., Zhu, X., Zhao, Y., Shu, J., Yuan, Q., Bhat, N. D. R., Cameron, A. D., … (2022). High-precision search for dark photon dark matter with the Parkes Pulsar Timing Array.

Rahimi, M., Pindor, B., Line, J. L. B., Barry, N., Trott, C. M., Webster, R. L., Jordan, C. H., … (2021). Epoch of reionization power spectrum limits from Murchison Widefield Array data targeted at EoR1 field.

Devarajan, H. R., Singh, S. B., & Howard, E. (2024). Explainable AI for cloud-based machine learning interpretable models and transparency in decision making.

Md. Touhidul Islam | AI in Business | Best Researcher Award

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Assist. Prof. Dr. Md. Touhidul Islam | AI in Business | Best Researcher Award

Assistant Professor at NPI University of Bangladesh | Bangladesh

Assist. Prof. Dr. Md. Touhidul Islam is a dedicated academic and researcher in Business Administration whose work spans marketing thought, strategic service excellence, customer behavior, and the evolving dynamics of online commerce. His professional identity is shaped by a commitment to student-centered teaching, scholarly contribution, and institutional development, reflected in his extensive involvement in academic instruction, curriculum enhancement, quality assurance activities, and mentorship. He has built a strong research footprint across areas such as service marketing, customer satisfaction, sustainable online business practices, agile and neuro-marketing, corporate responsibility, product and service quality, and digital consumer experiences, contributing numerous studies to international journals and collaborating on diverse research initiatives in emerging marketing contexts. His publications explore themes including cashless economies, ethical leadership, blockchain opportunities in hospitality, green innovations, artificial intelligence in business, logistics strategies, and cross-industry consumer perceptions, illustrating both analytical breadth and practical relevance. Beyond journal work, he has authored an academic book on Human Resource Information Systems and contributed to a globally published book chapter on AI-driven transformations in customer relationship service, extending his influence into specialized business and technology domains. His teaching philosophy emphasizes education as a purposeful and student-driven service that nurtures critical thinking, real-world application, and intellectual curiosity. He champions blended and flexible teaching methods, the use of contemporary business examples, and learning environments built on receptiveness, engagement, and reflective practice. Alongside teaching and research, he contributes to the global academic community through editorial and reviewer roles for multiple international journals, helping uphold scholarly rigor and support emerging researchers. Continuously expanding his academic horizon through advanced research and interdisciplinary inquiry, he represents a blend of pedagogical commitment, research-driven insight, and forward-looking academic leadership.

Profile: Google Scholar

Featured Publications

Islam, M. T., Hasan, M. M., Redwanuzzaman, M., & Hossain, M. K. (2024). Practices of artificial intelligence to improve the business in Bangladesh.

Islam, M. T. (2023). Newly developed green technology innovations in business: Paving the way toward sustainability.

Islam, M. T. (2019). Future impact of 4G on business in Bangladesh.

Islam, M. T. (2019). Market failure: Reasons and its accomplishments.

Islam, M. T., & Hasan, M. T. (2016). Corporate social responsibility of commercial banks in Bangladesh: A comparative study on nationalized and private banks.

Xuewen Dong | Network Security | Best Researcher Award

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Prof. Xuewen Dong | Network Security | Best Researcher Award

Professor at Xidian University | China

Professor Xuewen Dong is a distinguished scholar recognized for his influential contributions to wireless network security, AI security, and service intelligence, playing a pivotal role in advancing secure and intelligent computing technologies. His research spans a wide spectrum of critical domains, including mobile edge computing, blockchain scalability, adversarial machine learning, differential privacy, federated learning, and large-scale distributed systems. Through his extensive publication record in leading international journals and premier global conferences, he has consistently delivered innovative solutions that address emerging challenges in data privacy, intelligent connectivity, and trustworthy AI. His work on autonomous aerial vehicle–assisted computing, backdoor attack modeling, privacy-attack frameworks, and high-performance blockchain mechanisms demonstrates a unique ability to merge theoretical rigor with practical applicability, contributing significantly to the evolution of next-generation digital ecosystems. Beyond his research achievements, he is widely respected for his leadership within the academic community, offering strategic guidance, fostering collaborative research environments, and supporting interdisciplinary advancements across intelligent security technologies. His roles in major research centers and professional committees highlight his dedication to shaping technological development, mentoring the next generation of innovators, and strengthening global standards in secure computing practices. Over the course of his accomplished career, he has earned multiple prestigious recognitions for technological innovation, excellence in computing research, contributions to regional software development, and impactful guidance in academic competitions. These honors reflect his enduring influence and the far-reaching impact of his work across the fields of computer science and intelligent systems. With a strong commitment to scientific progress, innovation, and the responsible advancement of digital technologies, Professor Dong continues to be a driving force in the global pursuit of secure, adaptive, and intelligent computational infrastructures.

Profile: Google Scholar

Featured Publications

Tong, W., Dong, X., & Zheng, J. (2019). Trust-PBFT: A peer-trust-based practical Byzantine consensus algorithm.

Tong, W., Dong, X., Shen, Y., & Jiang, X. (2019). A hierarchical sharding protocol for multi-domain IoT blockchains.

Dong, X., Wu, F., Faree, A., Guo, D., Shen, Y., & Ma, J. (2019). Selfholding: A combined attack model using selfish mining with block withholding attack.

Yang, L., Dong, X., Xing, S., Zheng, J., Gu, X., & Song, X. (2019). An abnormal transaction detection mechanism on Bitcoin.

Gao, S., Chen, X., Zhu, J., Dong, X., & Ma, J. (2022). TrustWorker: A trustworthy and privacy-preserving worker selection scheme for blockchain-based crowdsensing.

Mahendra Gaikwad | Machine Learning | Best Researcher Award

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Dr. Mahendra Gaikwad | Machine Learning | Best Researcher Award

Assistant Professor at Veermata Jijabai Technological Institute (VJTI) | Mumbai | India

Dr. Mahendra Uttam Gaikwad is a forward-thinking mechanical and manufacturing engineering professional whose work reflects a deep commitment to advancing modern machining, smart materials research, sustainable manufacturing, and AI-driven optimization in industrial systems. Renowned for his ability to bridge theoretical innovation with practical engineering applications, he has built a strong scholarly footprint through impactful publications in SCI and Scopus-indexed journals, contributions to influential book chapters, and editorial leadership in notable international volumes focused on advanced materials and digital-age manufacturing. His research explores critical themes such as electrical discharge machining, surface integrity analysis, optimization algorithms, additive manufacturing, fatigue modelling, and machine learning applications in production environments, consistently demonstrating an aptitude for tackling complex engineering challenges through empirical investigation and computational modelling. In addition to his academic contributions, he has shown commendable innovation through multiple national and international patents addressing smart systems, sustainable material utilization, and intelligent manufacturing solutions. He has also been an active collaborator with academic institutions, research groups, and industry partners, contributing to advancements in machining automation, performance benchmarking, and data-driven design methodologies. A dedicated mentor, he has guided numerous undergraduate and postgraduate research projects, fostering a research-oriented learning environment and supporting the next generation of engineers. His work as a reviewer, conference contributor, and knowledge disseminator further underscores his commitment to strengthening global engineering discourse. Known for his leadership qualities, professional integrity, and continuous pursuit of technological excellence, Dr. Gaikwad has earned recognition for his contributions to teaching and research, positioning himself as a noteworthy contributor to the evolving landscape of smart and sustainable manufacturing.

Profiles: ORCID | Google Scholar

Featured Publications

Gaikwad, M. U., Somatkar, A. A., Ghadge, M., Majumder, H., Shinde, A. M., & Lohakare, A. V. (2025). Effect of dry and wet machining environments on surface quality of Al6061 using particle swarm optimization (PSO).

Sargar, T., Gautam, N. K., Jadhav, A., & Gaikwad, M. U. (2025). A comparative investigation of kerf width during CO₂ and fiber laser machining of SS 316L material.

Khan, M. A. J., Pohekar, S. D., Bagade, P. M., Gaikwad, M. U., & Singh, M. (2025). CFD analysis of NACA 4415 marine propeller ducts for managing flow separation.

Nishandar, S. V., Pise, A. T., Bagade, P. M., Gaikwad, M. U., & Singh, A. (2025). Computational modelling and analysis of heat transfer enhancement in straight circular pipe with pulsating flow.

Gaikwad, M. U., Gaikwad, P. U., Ambhore, N., Sharma, A., & Bhosale, S. S. (2025). Powder bed additive manufacturing using machine learning algorithms for multidisciplinary applications: A review and outlook.