Prof. Mengmeng Liao | Computer Vision | Research Excellence Award

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Prof. Mengmeng Liao | Computer Vision | Research Excellence Award

Associate Professor | Shanghai University | China

Prof. Mengmeng Liao is an accomplished researcher in artificial intelligence, computer vision, pattern recognition, and image processing, with a strong record of contributions to both foundational and applied aspects of visual computing. His work focuses on developing robust algorithms for face recognition, multi-resolution modeling, adaptive subspace learning, and representation learning, addressing complex challenges in real-world environments such as noise interference, limited samples, and multi-pose variation. He has authored more than 20 SCI/EI-indexed research papers, including publications in leading international journals such as Information Sciences, Neurocomputing, Expert Systems with Applications, Electronics, and IEEE Signal Processing Letters. His research impact is reflected in Scopus metrics, with 170 citations across 159 citing documents and an h-index of 6, alongside a growing presence on Google Scholar. Prof. Liao has also contributed to several major national research initiatives, securing competitive funding from programs such as the National Natural Science Foundation and the Postdoctoral Innovative Talent Support Program. His active engagement with the global academic community includes serving as a technical committee member, session chair, and program chair for numerous international conferences. Through his interdisciplinary approach and sustained research output, Prof. Liao continues to advance the field of artificial intelligence, particularly in intelligent visual perception, pattern learning, and computational recognition systems.

Publication Profile

Scopus

Publications

Fan, X., Liao, M., Chen, L., & Hu, J. (2023). Few-shot learning for multi-POSE face recognition via hypergraph de-deflection and multi-task collaborative optimization. Electronics.

Liao, M., Fan, X., Li, Y., & Gao, M. (2023). Noise-related face image recognition based on double dictionary transform learning. Information Sciences.

Fan, X., Liao, M., Xue, J., Wu, H., Jin, L., Zhao, J., & Zhu, L. (2023). Joint coupled representation and homogeneous reconstruction for multi-resolution small sample face recognition. Neurocomputing.

Liao, M., Li, Y., & Gao, M. (2022). Graph-based adaptive and discriminative subspace learning for face image clustering. Expert Systems with Applications.

Jiang, W., Li, Y., Liao, M., & Wang, S. (2021). An improved LPI radar waveform recognition framework with LDC-Unet and SSR-Loss. IEEE Signal Processing Letters.

 

Dr. Yingguo Lyu | Biological Sciences | Innovative Research Award

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Dr. Yingguo Lyu | Biological Sciences | Innovative Research Award

Henan University of Technology | China

Dr. Yingguo Lyu is a food scientist specializing in cereal chemistry, noodle technology, and the processing principles of traditional Chinese foods. His research focuses on understanding the biochemical, rheological, and structural behaviors of cereal-based products and developing innovative technologies for improving the quality, safety, and industrialization of staple foods such as noodles, steamed bread, dumpling wrappers, and instant products. He has contributed significantly to the study of frozen dough systems, moisture dynamics, gluten network formation, fermentation processes, and multi-grain formulations. A major part of his work explores rice bran glutamate decarboxylase, GABA production, and enzyme-modulated pathways that support functional food development. Dr. Lyu has led and contributed to multiple scientific projects on fresh noodle color stability, circular drying technology, noodle industrialization, and protein-matrix interactions, producing practical outcomes for China’s grain-processing sector. His research achievements include award-winning technological advancements and several patents related to frozen noodles and GABA-rich food innovations. He has authored numerous high-impact journal articles and books in the fields of food science and cereal technology. His scholarly contributions are widely cited, with measurable visibility across academic platforms. Based on publicly available citation databases, his Scopus and Google Scholar citation records indicate strong research influence, including an h-index that reflects his sustained contributions to cereal chemistry and food engineering. His body of work continues to bridge fundamental food science with industrial applications, supporting advancements in modern food processing technologies.

Publication Profile

ORCID

Featured Publications

Lyu, Y., Chen, J., Li, X. (2014). Study on processing and quality improvement of frozen noodles. LWT – Food Science and Technology, 59(1), 403-410.

Mr. Carlos Rodrigo Paredes Ocranza | Affective Computing | Machine Learning Research Award

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Mr. Carlos Rodrigo Paredes Ocranza | Affective Computing | Machine Learning Research Award

Zhejiang University of Science and Technology | China

Mr. Carlos Rodrigo Paredes Ocaranza is an emerging researcher in artificial intelligence with a strong focus on EEG-based emotion recognition, affective computing, and brain–computer interface (BCI) analytics. His work challenges conventional assumptions in neurotechnology by demonstrating that traditional machine learning pipelines, when paired with domain-specific feature engineering, outperform state-of-the-art deep learning models such as EEGNet for consumer-grade EEG devices. His research introduces advanced domain adaptation methods—such as anatomical channel mapping, CORAL, and TCA—that collectively achieve remarkable gains in cross-dataset generalization, including a reported 69-fold improvement in robustness. He has conducted large-scale validation experiments across hundreds of independent evaluations to ensure statistical reliability and real-world applicability. His contributions highlight significant computational advantages, including faster model training, reduced inference time, and lower memory requirements, advancing the feasibility of accessible BCI systems for mental-health monitoring and multimodal emotion-decoding research. His citation profile is currently emerging, with one indexed publication in Scopus and expanding coverage as new profiles on Google Scholar and ORCID are being established. His scholarly documents, publication records, and citation metrics continue to grow as his research outputs undergo indexing in major academic databases. His work reflects a dedication to developing practical, interpretable, and resource-efficient neuro-AI systems that can be deployed beyond laboratory environments, strengthening the intersection between cognitive science, statistical learning, and computational affective modeling.

Publication Profile

ORCID

Featured Publication

Paredes Ocaranza, C. R., Paredes Ocaranza, E. D., & Yun, B. (2025). Traditional machine learning outperforms EEGNet for consumer-grade EEG emotion recognition: A comprehensive evaluation with cross-dataset validation. Sensors, 25(23), 7262.

Prof. Dr. Beatriz Defez | Sensors | Research Excellence Award

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Prof. Dr. Beatriz Defez | Sensors | Research Excellence Award

Professor | Valencia Polytechnic University | Spain

Beatriz Defez García is an accomplished scholar whose work bridges engineering graphics, materials science, acoustic behavior, assistive technologies, and advanced computational modeling. Her research notably spans finite-element analysis of ceramic materials, acoustic characterization of engineered structures, fluid–solid interactions, and the development of innovative navigation systems to support individuals with visual impairments. She has made influential contributions to the fields of human–machine interaction, sensory guidance technologies, telemedicine, and computer-assisted image segmentation, particularly in dermatological applications. Her scholarship encompasses collaborative, interdisciplinary projects addressing societal and industrial needs through engineering innovation. With a strong record of productivity, she has authored 30 Scopus-indexed documents and accumulated 188 citations in Scopus, reflecting an h-index of 7, while her Google Scholar profile reports 486 citations, an h-index of 11, and an i10-index of 11, demonstrating the sustained visibility and relevance of her research. Her most widely cited studies include pioneering prototypes for real-time assistive navigation, analytical and experimental investigations of bubble and drop oscillations, and advanced techniques for usability evaluation in digital learning environments. Across her publications, Defez García integrates computational methods, experimental validation, and user-centered design principles to advance engineering solutions that improve material performance, enhance accessibility, and support health-related digital transformation. Her recent work also highlights growing contributions in telemedicine, sustainable digitalization, and biomedical image processing, reinforcing her role as a multidisciplinary researcher shaping both technological innovation and applied scientific practice.

Publication Profile

Scopus | ORCID | Google Scholar

Featured Publications

Dunai, L., Fajarnes, G. P., Praderas, V. S., Defez Garcia, B., & Lengua, I. (2010). Real-time assistance prototype—A new navigation aid for blind people. IECON Annual Conference on IEEE Industrial Electronics Society, 1–6.

Milne, A. J. B., Defez, B., Cabrerizo-Vílchez, M., & Amirfazli, A. (2014). Understanding (sessile/constrained) bubble and drop oscillations. Advances in Colloid and Interface Science, 203, 22–36.

Dunai, L., Peris-Fajarnés, G., Lluna, E., & Defez, B. (2013). Sensory navigation device for blind people. The Journal of Navigation, 66(3), 349–362.

Fraile-Garcia, E., Ferreiro-Cabello, J., Defez, B., & Peris-Fajanes, G. (2016). Acoustic behavior of hollow blocks and bricks made of concrete doped with waste-tire rubber. Materials, 9(12), 962.

Maria, M. S., Silvia, A. N., Beatriz, D. G., Andrew, D., & Guillermo, P. F. (2022). Health care in rural areas: Proposal of a new telemedicine program assisted from the reference health centers, for sustainable digitization and its contribution to carbon reduction. Heliyon, 8(7).

Mr. Ikram Ullah | Biological Sciences | Research Excellence Award

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Mr. Ikram Ullah | Biological Sciences | Research Excellence Award

Northwest A&F University | China

Mr. Ikram Ullah is an emerging life science researcher specializing in plant genomics, molecular biology, and stress physiology, with a particular focus on horticultural crops. His research centers on genome-wide identification, characterization, and functional analysis of key gene and transcription factor families that regulate plant growth, defense pathways, and stress responses. He has contributed significantly to understanding bHLH, CRK, YABBY, callose enzyme, and Tubby-Like Protein gene families, providing new insights into their structural diversity, expression dynamics, and regulatory roles under biotic and abiotic stresses. His work extends to plant–pathogen interactions, root microbiota, cold stress management, heat stress mitigation, and hormonal modulation in economically important crops such as rose, cucumber, wheat, pepper, Brassica species, and tuberose. He is skilled in advanced molecular techniques, gene expression profiling, microbial metagenomics, and bioinformatics tools that support integrative genomic analyses. His research contributions are reflected in his citation metrics, with notable visibility across global indexing platforms. According to Scopus, his work has received around 1,900 citations from approximately 1,606 documents with an h-index of 23. His broader academic footprint on Google Scholar further highlights his growing impact within plant sciences and biotechnology. Mr. Ullah’s research continues to advance molecular breeding, stress-resilience strategies, and genomic understanding essential for sustainable horticulture and crop improvement.

Publication Profile

Scopus | ORCID

Featured Publications

Ullah, I., Yuan, W., Uzair, M., Li, S., Rehman, O., Nanda, S., & Wu, H. (2022). Genome-wide identification and expression analysis of the RcYABBYs reveals their potential functions in rose under Botrytis cinerea infection. Horticulturae, 8, 989.

Ullah, I., Ponsalven, A., Abbas, A., Hussain, S., & Nanda, S. (2022). Molecular characterization of bHLH transcription factor family in rose under Botrytis cinerea infection.

Ullah, I., et al. (2025). Molecular mechanisms and genomic strategies for enhancing stress resilience in pepper crop. Scientia Horticulturae, 352, 114403.

Tang, Y., Wu, J., Zhao, M., Guo, Y., Ullah, I., & Wu, H. (2020). Complete genome sequence of begonia flower breaking virus, a novel member of the genus Potyvirus. Archives of Virology, 165, 1915–1918.

Nanda, S., Rout, P., Ullah, I., Swapna, R., Velagala, V. R., Ritesh, K., & Wu, H. (2023). Genome-wide identification and molecular characterization of CRK gene family in cucumber under cold stress and sclerotium rolfsii infection. BMC Genomics, 24, 219.

Dr. Shuihuan Guo | Agriculture Technology | Research Excellence Award

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Dr. Shuihuan Guo | Agriculture Technology | Research Excellence Award

Henan Agricultural University | China

Dr. Shuihuan Guo is a food science researcher specializing in fruit and vegetable storage, postharvest physiology, and the molecular biology of horticultural crops, with a strong focus on grapes. His work explores the mechanisms underlying stress responses, anthocyanin biosynthesis, quality formation, and the regulation of plant metabolic pathways that influence fruit development and postharvest quality. He has contributed significantly to understanding how environmental cues, hormonal signaling pathways, and gene regulatory networks shape the biochemical and physiological traits of grape berries under both natural and controlled conditions. His research particularly emphasizes drought stress, abscisic acid (ABA) signaling, melatonin biosynthesis, and microRNA-mediated regulatory mechanisms related to berry coloration, metabolite accumulation, and stress tolerance in different grape genotypes. Through integrating advanced molecular techniques with applied cultivation strategies, he aims to enhance fruit quality, improve resilience to abiotic stress, and support sustainable viticulture practices. His scientific contributions are recognized through measurable academic impact, including Scopus metrics of 219 citations, 15 documents, and an h-index of 8, supported by growing citation activity across Google Scholar as well. His publications appear in high-impact journals in horticulture, food chemistry, and plant molecular biology, reflecting his collaborative work and his commitment to advancing postharvest and viticultural science. Overall, his research bridges fundamental molecular insights with practical applications to support high-quality production and efficient cultivation of grapes and other horticultural crops.

Publication Profile

Scopus

Featured Publications 

Guo, S., Zhang, M., Feng, M., et al. (2024). miR156b-targeted VvSBP8/13 functions downstream of the ABA signal to regulate anthocyanin biosynthesis in grapevine fruit under drought. Horticulture Research, 11, 293.

Guo, S., Xu, T., Shi, T., et al. (2020). Cluster bagging promotes melatonin biosynthesis in the berry skins of Vitis vinifera cv. Cabernet Sauvignon and Carignan during development and ripening. Food Chemistry, 305, 125502.

Guo, S., Xu, T., Ju, Y., et al. (2023). MicroRNAs behave differently to drought stress in drought-tolerant and drought-sensitive grape genotypes. Environmental and Experimental Botany, 207, 105233–105248.

Guo, S., Yang, B., Wang, X., et al. (2021). ABA signaling plays a key role in regulated deficit irrigation-driven anthocyanins accumulation in ‘Cabernet Sauvignon’ grape berries. Environmental and Experimental Botany, 181, 104290.

Dr. Yan Gao | Mems Sensors | Research Excellence Award

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Dr. Yan Gao | Mems Sensors | Research Excellence Award

Professor | Sun Yat-sen University | China

Dr. Yan Gao is a distinguished researcher in geotechnical engineering whose work advances fundamental understanding of the micromechanical behavior of geomaterials, particularly aging, creep, particle breakage, and structuration effects in sand. His research integrates advanced numerical simulations using the Discrete Element Method with innovative laboratory characterization techniques, including wave-based measurements, tactile pressure sensors, and MEMS-enabled Smart Soil Particles (SSP). Through these approaches, he uncovers the micro–macro mechanisms governing deformation, stiffness evolution, and kinematic responses in granular soils under various loading conditions. His investigations also extend to the development and application of GeoMEMS technologies for monitoring geologic hazards and improving early warning systems for urban rail transit. Additionally, he contributes to sustainable geotechnical engineering through research on novel and eco-friendly building materials. Dr. Gao’s scientific contributions have resulted in significant academic visibility, reflected in 158 Scopus citations, 144 citing documents, 42 publications, and an h-index of 8 on Scopus, with complementary recognition on Google Scholar. His work provides crucial insights for infrastructure safety, coastal and marine geotechnics, foundation engineering in calcareous sands, and next-generation smart sensing technologies in geomechanics.

Publication Profile

Scopus

Featured Publications

Gao, Y., Shi, T. G., Yuan, Q., Shi, X., & Sun, K. T. (2024). Particle gradation effects on creep characteristics and the underlying mechanism in calcareous sand. Construction and Building Materials, 424, 135952.

Gao, Y., Shi, T. G., Yuan, Q., & Sun, K. T. (2024). The creep characteristics and related evolution of particle morphology for calcareous sand. Powder Technology, 431, 119077.

Gao, Y., Shi, X., Yuan, Q., Sun, L., & Sun, K. T. (2024). Particle breakage and uneven settlement characteristics of calcareous sand foundation. Journal of Building Engineering, 100, 111662.

Gao, Y., Chen, Q., Yuan, Q., & Wang, Y. H. (2023). The kinematics and micro mechanism of creep in sand based on DEM simulations. Computers and Geotechnics, 153, 105082.

Gao, Y., & Wang, Y. H. (2016). Experimental characterization of deformation, stiffness, and contact force distributions of sand during secondary compression and rebound. Canadian Geotechnical Journal, 53(5), 889–898.

Prof. Dr. Gary Wong | Computer Science Education | Research Excellence Award

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Prof. Dr. Gary Wong | Computer Science Education | Research Excellence Award

Faculty of Education | The University of Hong Kong | Hong Kong

Prof. Dr. Gary Ka Wai Wong is a leading scholar in computational thinking, computer science education, and artificial intelligence (AI)–enhanced learning, widely recognized for his interdisciplinary contributions to digital literacy, immersive learning environments, and K–12 STEM innovation. His research explores how constructionist pedagogies, data-driven precision learning, and extended reality experiences foster cognitive development, problem-solving, and twenty-first century competencies in learners. He has played a pivotal role in shaping international frameworks for digital literacy and AI education, including large-scale studies that examine teachers’ technological adoption, students’ computational thinking development, and school-level implementation of coding and STEM initiatives. His body of work reflects strong methodological diversity, spanning design-based research, meta-analysis, psychometric assessment, and mixed-methods evaluations of learning technologies. His scholarly influence is demonstrated by substantial global citations: Scopus records over 1,218 citations across 1,071 citing documents with an h-index of 18, while Google Scholar reports more than 3,111 citations with an h-index of 24 and 48 i10-index publications. His contributions extend to immersive virtual reality in science learning, digital competence assessment, and early-age unplugged and plugged computational thinking activities. His research continues to inform international policy, curriculum innovations, and empirical understandings of how AI, coding, and digital tools can be meaningfully integrated into education to cultivate future-ready learners.

Publication Profile

Scopus | ORCID | Google Scholar

Featured Publications 

Wong, G. K. W., Ma, X., Dillenbourg, P., & Huan, J. (2020). Broadening artificial intelligence education in K–12: Where to start? ACM Inroads, 11(1), 20–29.

Saxena, A., Lo, C. K., Hew, K. F., & Wong, G. K. W. (2020). Designing unplugged and plugged activities to cultivate computational thinking: An exploratory study in early childhood education. The Asia-Pacific Education Researcher, 29(1), 55–66.

Wong, G. K. W., & Cheung, H. Y. (2020). Exploring children’s perceptions of developing twenty-first century skills through computational thinking and programming. Interactive Learning Environments, 28(4), 438–450.

Law, N. W. Y., Woo, D. J., De la Torre, J., & Wong, K. W. G. (2018). A global framework of reference on digital literacy skills for indicator 4.4.2. UNESCO Institute for Statistics.

Lui, A. L. C., Not, C., & Wong, G. K. W. (2023). Theory-based learning design with immersive virtual reality in science education: A systematic review. Journal of Science Education and Technology, 32(3), 390–432.

Mr. Xingwang Bian | Traveling Wave Tube | Research Excellence Award

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Mr. Xingwang Bian | Traveling Wave Tube | Research Excellence Award

Senior Engineer | Beijing Vacuum Electronics Research Institute | China

Mr. Xingwang Bian is a China-based researcher whose work significantly advances the field of vacuum electronics, high-frequency device engineering, and terahertz (THz) communication technologies. His research focuses on the design, optimization, and experimental characterization of G-band traveling wave tubes (TWTs), slow-wave structures, and high-power millimeter-wave sources that support next-generation radar, wireless communication, and sensing systems. Bian has contributed to innovations in modified folded waveguide configurations, wide-band continuous-wave amplifiers, pulsed-wave THz devices, and hybrid photonic–electronic architectures that enable communication beyond 300 GHz. His publications demonstrate strong expertise in device modeling, electromagnetic design, vacuum electronic component fabrication, and system-level performance enhancement. With 17 research documents indexed and 155 citations in Scopus, along with citations registered across 118 scholarly works, Bian has established a growing academic footprint. His h-index in Scopus is 4, reflecting impactful early-career contributions in a highly specialized domain. His citation metrics in Google Scholar also highlight the visibility and technical relevance of his work within the global vacuum electronics and THz technology communities. Through collaborative projects, experimental demonstrations, and high-frequency device innovations, Bian’s research continues to support emerging breakthroughs in ultra-high-speed wireless communication and advanced radar systems. His contributions underscore the importance of compact, efficient, and high-power THz sources that can shape the future landscape of communication engineering and microwave electronics.

Publication Profile

Scopus | ORCID

Featured Publications 

Bian, X., Pan, P., Du, X., Feng, Y., Li, Y., Song, B., & Feng, J. (2025). Design and experiment of modified folded waveguide slow wave structure for 60-W G-band traveling wave tube. IEEE Microwave and Wireless Technology Letters.

Bian, X., Pan, P., Xian, S., Yang, D., Zhang, L., Cai, J., & Feng, J. (2025). A G-band pulsed wave traveling wave tube for THz radar. Preprints.

Zhu, M., Cai, Y., Zhang, L., Zhang, J., Hua, B., Ma, K., Ding, J., Bian, X., et al. (2025). Surpassing kilometer-scale terahertz wireless communication beyond 300 GHz enabled by hybrid photonic–electronic synergy. Research Square.

Bian, X., Pan, P., Du, X., Song, B., Zhang, L., Cai, J., & Feng, J. (2024). Demonstration of a high-efficiency and wide-band 30-W G-band continuous wave traveling wave tube. IEEE Electron Device Letters.

Feng, Y., Bian, X., Song, B., Li, Y., Pan, P., & Feng, J. (2022). A G-band broadband continuous wave traveling wave tube for wireless communications. Micromachines.

Mr. Ro-Yu Wu | Computer Science | Research Excellence Award

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Mr. Ro-Yu Wu | Computer Science | Research Excellence Award

Professor | Lunghwa University of Science and Technology | Taiwan

Mr. Ro-Yu Wu is a Taiwan-based researcher recognized for his contributions to combinatorial algorithms, graph theory, and efficient data structures, particularly within the domains of ranking and unranking methods, Hamiltonian graph properties, and algorithmic generation of combinatorial objects. As a productive scholar in theoretical computer science and industrial management, his work emphasizes the design of loopless, lexicographic, and Gray code–based algorithms that enhance computational efficiency and fault-tolerant communication in complex networks. His research is well acknowledged in the global academic community, as reflected in his Scopus record with 45 documents, 308 citations across 189 citing works, and an h-index of 10. On ResearchGate, he maintains an active profile with 47 publications, over 6,300 reads, and 367 citations. These metrics highlight his growing influence, especially in areas involving structured graph traversal, spanning-tree generation, and the analytical foundations supporting optimization and data broadcasting systems. His work frequently explores practical algorithmic strategies for high-performance computing environments, providing innovative insights for fault-tolerant network design and combinatorial enumeration. Mr. Wu’s collaborations span multi-author research teams, contributing to advancements published in high-impact venues such as Theoretical Computer Science, The Journal of Supercomputing, Journal of Combinatorial Optimization, and Optimization Letters. His ongoing research continues to shape efficient computational paradigms for combinatorial structures, making him a relevant contributor to the future of theoretical and applied algorithmic studies.

Profile

Scopus

Featured Publications 

Xie, Z., Wu, R.-Y., & Shi, L. (2025). Ranking and unranking algorithms for derangements based on lexicographical order. Theoretical Computer Science.

Pai, K.-J., Wu, R.-Y., Peng, S. L., & Chang, J. M. (2023). Three edge-disjoint Hamiltonian cycles in crossed cubes with applications to fault-tolerant data broadcasting. The Journal of Supercomputing.

Chang, Y. H., Wu, R.-Y., Chang, R. S., & Chang, J. M. (2022). Improved algorithms for ranking and unranking (k, m)-ary trees in B-order. Journal of Combinatorial Optimization.

Wu, R.-Y., Tseng, C. C., Hung, L. J., & Chang, J. M. (2022). Generating spanning-tree sequences of a fan graph in lexicographic order and ranking/unranking algorithms. International Symposium on Combinatorial Optimization.

Chang, Y. H., Wu, R.-Y., Lin, C. K., & Chang, J. M. (2021). A loopless algorithm for generating (k, m)-ary trees in Gray code order. Optimization Letters.