Dr. Liquan Huang, Professor

College of Life Sciences, Zhejiang University, Hangzhou, China

Topic: The Role of Taste Signaling Proteins in the Oral Cavity and Beyond

View More

2024 10th International Conference on Biology and Life Sciences

Nov. 24-26, 2024

Taste is one of the five special senses humans possess. Sweet, bitter, umami, sour and salty are the five primary taste qualities that are evaluated before food is ingested. Taste sensation begins at taste buds in the oral cavity. Interactions of sapid molecules in foodstuffs with receptors on taste bud cells initiate taste signaling cascades. Sweet, bitter and umami tastants are detected by G protein-coupled receptors (GPCRs) whereas sour and salty molecules are sensed by ion channels. Humans have one dimeric GPCR each for sweet and umami tasting substances, respectively, and 26 monomeric GPCRs for bitter tasting substances. Activation of these GPCRs triggers G protein-mediated signal transduction, followed by the release of the transmitter ATP onto the afferent nerve fibers. As an end organ of the peripheral gustatory system, taste buds undergo initial intragemmal signal processing, which can be modulated by hormones, cytokines as well as transmitters from the nervous system. More recently, taste receptors and their downstream signaling proteins have been found in the extraoral organs, including the gastrointestinal tract, pancreas, respiratory airways, lung, gallbladder and thymus. Particularly, a type of rare epithelial cells, tuft cells, resembles most to some taste bud cells in terms of gene expression profiles. These tuft cells can utilize taste GPCRs and other signaling proteins to sense noxious stimuli and invading pathogens, such as allergens, bacteria, protists, and helminths, and initiate innate immune responses to eliminate these agents. Under some circumstances, however, tuft cells can also play a protective role by preventing cell death or facilitating inflammation resolution to avoid cytokine storms. Further investigations on taste bud cells, tuft cells and other taste signaling protein-expressing cells may reveal more functions of these taste signal proteins in regulating immune responses and modulating neuronal functions.

Dr. Liquan Huang received a Bachelor degree in Biology and a Master degree in Molecular Genetics from Zhejiang University, and Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, respectively. He then studied at Yale University and obtained a PhD in Molecular Biology. He carried out his postdoctoral research at a Howard Hughes Medical Institute laboratory at New York University, and established his own laboratory at the Monell Chemical Senses Center affiliated with the University of Pennsylvania. After returning to Zhejiang University, he has been supervising a chemosensory biology laboratory. His research has been focused on the biology of taste and smell, which has been funded by NIH, NSFC, MOST and US-Israel Binational Science Foundation. His findings have been published in high-impact journals such as Nature Genetics, Nature Neuroscience, PNAS, and eLife.

Dr. Chen-Wei Chen, Associate Professor

Department of Marine Structures, Ship Engineering and Ocean Engineering, Ocean College, Zhejiang University, Hangzhou, China

Topic: Research and Application of High-Performance Energy-Saving Kappel Propeller for Ship and Underwater Vehicle

View More

2024 9th International Conference on Civil and Environmental Engineering

Nov. 24-26, 2024

The study introduced the state-of-art of marine propulsion system and Kappel propeller; Concept Design for a high-performance energy-saving Kappel propeller; Application of Kappel propeller and CRP on Marine Ships and Underwater Vehicle. CFD study of energy-saving enhanced performance for Kappel propellers on hydrodynamics, cavitation and noises, etc.

Dr. Chen-Wei Chen was born in 1980 in Taipei, Taiwan of China. Currently, he is an Associate Professor of Department of Naval Architecture and Ocean Engineering, Ocean College, Zhejiang University, Zhoushan, P.R. China, since 2013. He received the Ph.D. degree at the Department of Engineering Science and Ocean Engineering, National Taiwan University (NTU), Taipei, Taiwan in 2013 and received the marine engineering degree in naval architecture and marine engineering from the National Cheng Kung University (NCKU), Tainan of Taiwan. His research interests include Hydrodynamics, System Dynamics, Maneuvering and Seakeeping performance of marine vehicle (ship and submarine) and ocean platform, etc. There is in-depth research in these research fields including design and development of special propeller propulsion system, active wave compensation system, novel submarine vehicle and ocean platform motion control and satellite positioning monitoring measurement technology for ships, etc. In the past year, he serves as a Reviewer for peer-reviewed journals, including Ocean Engineering, IEEE Journal of Oceanic Engineering, Journal of Navigation, Journal of Sensors, Journal of Marine Science and Technology, Journal of Marine Science and Application, Journal of Marine Science and Engineering and Journal of Applied Sciences, etc. Simultaneously invited to serve as a special issue editor of Applied Sciences in Special Issue title of Computational Fluid Dynamics-based for Ship Hydrodynamics Applications. In recent years, he has published over 30 high-level related SCI and/or EI research papers.

Dr. Norhidayu Kasim, Associate Professor

Department of Civil Engineering, Faculty of Engineering, Islamic University of Malaysia, Gombak, Malaysia

Topic: Adapting Rainfall Thresholds for Landslide Prediction: A Comprehensive Approach for Early Warning Systems

View More

2024 9th International Conference on Civil and Environmental Engineering

Nov. 24-26, 2024

Rainfall-induced landslides are a critical challenge faced by many regions worldwide, posing severe threats to human life, infrastructure, and the environment. As extreme weather events become more frequent due to climate change, there is an urgent need for more effective methods to predict and mitigate the impacts of landslides. Universal rainfall thresholds should be developed and adapted in various climates and geological settings to forecast landslides and support the establishment of early warning systems. Through an analysis of landslide-triggering rainfall data from multiple case studies, this research proposes two essential thresholds which are the Intensity-Duration threshold (I-D) and Cumulative-Duration threshold (C-D). These thresholds serve as predictive tools by identifying critical rainfall patterns often preceding landslide occurrences. To enhance their accuracy, advanced computational techniques such as Artificial Neural Networks (ANNs) are employed, allowing for the modelling of complex relationships between rainfall intensity, duration, and the likelihood of landslides. In addition, the study examines how key factors such as soil characteristics, rock type, and slope dynamics contribute to regional variations in landslide susceptibility. By integrating these factors into rainfall threshold models, it becomes possible to develop highly localized early warning systems capable of providing timely alerts and reducing the risk of disaster. This will explore how these global thresholds can be adapted to different regions and environments, offering a critical tool for governments and disaster management agencies. The aim is to enhance preparedness, reduce casualties, and safeguard communities from the devastating effects of rainfall-induced landslides.

Dr. Norhidayu Kasim is an associate professor, practised engineer as well as researcher with a deep focus on geotechnical engineering, specializing in rainfall-induced landslides and early warning systems. With extensive experience in analyzing geological and environmental hazards, she has contributed significantly to the development of effective prediction models for landslides, particularly in regions prone to extreme weather events. Her expertise spans across multiple disciplines, including civil engineering, geology, and climate science, where she integrates advanced computational tools to tackle complex environmental challenges. She has been actively involved in various research projects to improve disaster preparedness and reduce the impact of natural hazards on communities. She has a solid background in employing Artificial Neural Networks (ANNs) to enhance the precision of forecasting models, which have proven instrumental in identifying critical rainfall patterns linked to landslide occurrences. Her research combines empirical data with cutting-edge technologies, leading to the creation of adaptable rainfall thresholds applicable in diverse geological settings.

Dr. Xiaomei Guo, Professor

School of Management, Xiamen University, Xiamen, China

Topic: On Digital Business Model Transformation - Evidence from a Survey in China

View More

2024 9th International Conference on Economics, Finance and Management Science

Nov. 22-24, 2024

The speech explores the evolving landscape of business models in the context of digitalization, supported by empirical data from a survey conducted in China. This study is jointly conducted with the IMA China research team, highlighting significant insights into the digital transformation in the business model. It shows the position of companies in the matrix of digital business model, and the level of innovation in the different elements of business model.

Dr. Xiaomei Guo is a Professor of accounting department, School of Management, Xiamen University. PhD in Management (accounting), CICPA and CGMA. Director of The study center of Management accounting of Xiamen University. On board of the educational committee of IMA ,Deputy editor of CHINESE MANAGEMENT ACCOUNTING ,reviewer for Asian Review of Accounting. Consultant in Management accounting for the Finance Department of Fujian Provincial government and Member of North east Asia Management Accounting Leaders Think tank, CGMA100. Ex deputy director of the Accounting department of XMU. She was visiting scholar to Ivey Business School of Western University, Canada in 2017 and to Saint Mary’s University in 2000. Her research interest is in management accounting, especially in corporate governance and risk management, management controls and digital transformation. Major publications included: Collection of Cases in management accounting Practice, On environmental management accounting practice , on FSSC model in digital times etc.

Dr. Jian Chen, Associate Professor

Belt and Road School, Beijing Normal University, Zhuhai, China

Topic: Integrating Social Dynamics in Water Resource Management

View More

2024 9th International Conference on Economics, Finance and Management Science

Nov. 22-24, 2024

Effective water resource management requires a comprehensive understanding of the interplay between social dynamics and ecological systems. This paper examines the role of human behaviors, community values, and governance structures in shaping sustainable water management practices. Utilizing the Social-Ecological Systems Framework, we analyze how social factors influence water use, access, and conservation efforts across diverse communities. By highlighting case studies that illustrate successful stakeholder engagement and collaborative governance, we demonstrate the potential for integrating social dynamics into water management strategies. Our findings suggest that fostering community involvement and aligning water management policies with local values can enhance both ecological health and social equity, ultimately leading to more resilient water systems. This approach not only addresses immediate water challenges but also promotes long-term sustainability and adaptability in the face of changing environmental conditions.

Dr. Jian Chen is an Associate Professor at Belt and Road School, and International Business Faculty, at Beijing Normal University, a Visiting Scholar at De Montfort University (UK) Business School, a Visiting Scholar at Gloucestershire University (UK), a Visiting Scholar supported by the Chinese National Scholarship Council(CSC), Research Fellow in the Institute of Energy and Sustainable Development and the School of Business and Law, De Montfort University (UK). Dr. Chen has participated in 4 international projects, 8 provincial projects, chaired 10 city projects, 7 school-level projects; 1 social science project in Hubei Province, and 2nd excellent economic and social development research project in Meizhou city Awards, Guangdong Province, presided over the University’s Teaching Quality Project of the school-level by the courses of: "Organizational Behavior", "Principles of Management", "Enterprise Strategic Management". In recent years, she has published 95 academic papers, including 3 SCI journals,5 CSSCI journals, 3 ISTP papers, and 4 core journals of CSTPCD, with 2 works, 1 textbook, and 3 joints. Also, as director of General Office and Admission Office, Belt and Road School, Beijing Normal University, and Green Industry Research Center of Beijing Normal University, Zhuhai, Chen concurrently acts as Member of Guangdong Rural Economics Association, Consultant of Zhuhai Credit Association, and Standing Director of Zhuhai Convention and Exhibition Industry Association. She has won teaching and research awards such as "Best Teaching Effects Teacher Evaluation Award", "3rd Prize Excellent Teaching Quality" and "Teaching and Research Excellence Award".

Dr. Shuo Zhao, Professor

School of International Studies, Communication University of China, Beijing, China

Topic: European Bilingual Education Policy and Model in Bologna Process

View More

2024 9th International Conference on Social Sciences and Humanities

Nov. 22-24, 2024

First it will introduce CLIL education policy in EU (Content and Language Integrated Learning).Then development of bilingual education in EU will be discussed. Based on bilingual education model curriculum design of bilingual education in European Union is expounded with case analysis of bilingual education in Luxemburg and France. Evaluation on bilingual education will be put forward at last.

Dr. Shuo Zhao is Professor in Communication University of China (CUC) and Fellow of New York Academy of Sciences while he is guest professor/PhD Superviser in University of Malaya(UM), Krirk University of Thailand and visiting professor in Universidad Nacional de Rosario(UNR) in Argentina. He received his two Ph.D in Shanghai International Studies University and Northwestern Polytechnical University. He did his Postdoctoral Research both in Fudan University, Shanghai of China and Universitat of Barcelona, Barcelona of Spain. He is majored in higher education, digital education and linguistic study. Professor Shuo Zhao ever presides over National Social Science Fund in China, China Postdoctoral Science Fund, Humanities and Social Science Fund of Chinese Ministry of Education, National Education Science Program. He publishes more than 100 papers and articles approximately. He is awarded as International Humanity Scholar by American Common Ground Publishing and CEU of Spain in 2014. In addition he is awarded as Emerging Humanity Scholar by American Common Ground Publishing and Imperial College London in 2017. Recently he is awarded as Emerging Scholar by Universidad Complutense of Spain in 2021, Emerging Scholar by University of Aegean of Greece in 2022, Emerging Scholar by University of Sorbonne of France in 2023, Emerging Scholar by Sapienza University of Rome of Italy in 2024.

Dr. Dayu Jiang, Associate Professor

Department of English, School of Foreign Languages and Literature, Wuhan University, Wuhan, China

Topic: Why Do We Need to Consider Cognitive Load in Mobile-Mediated Collaborative Learning?

View More

2024 9th International Conference on Education and Innovation

Nov. 22-24, 2024

Learners in computer or mobile-assisted collaborative learning environments could remain anonymous or unfamiliar, as educators might not provide explicit socializing activities with the assumption that effective interactions could happen naturally in these situations. However, from the perspectives of social presence, it was hypothesized that for learners who were unfamiliar to their peers in online collaborative learning environments, performing explicit socializing activities prior to learning phases would help them have better learning performance, experience lower levels of working memory load, and experience higher levels of social presence than performing implicit socializing activities. This research was conducted to test the hypotheses with 60 participants. The results showed that providing explicit socializing activities to unfamiliar learners in mobile-assisted collaborative learning environments increased the level of social presence, facilitated their acquisition of complex cognitive skills such as EFL argument essay writing skills, and reduced the level of cognitive load in learning. These results are discussed in light of human cognitive architecture and the social presence theory.

Dr. Dayu Jiang is an associate professor and Ph.D supervisor in Wuhan University, China. His research interests include cognitive load theory, instructional design, and applied linguistics. He published his monograph Cognitive Load Theory and Foreign Language Listening Comprehension with Springer Nature in August 2024. His recent publications appear in Educational Psychology Review, Computers and Education, British Journal of Educational Psychology, Educational Psychology, Computer Assisted Language Learning, among other Journals. He serves as a peer reviewer for a number of SSCI journals.

Dr. Martin Woesler, Professor

Jean Monnet Research Centre of Excellence, Foreign Studies College, Hunan Normal University, Changsha, China

Topic: Navigating Education into the New Age of Artificial Intelligence: A Global Perspective

View More

2024 9th International Conference on Education and Innovation

Nov. 22-24, 2024

Globalization has significantly accelerated the development of transformative technologies such as blockchain, quantum communication, generative artificial intelligence (AI), and augmented/virtual reality (AR/VR). The recent decision by the United States to restrict China's access to generative AI technologies has reshaped the competitive landscape, reminiscent of previous challenges faced by China with the Huawei operating system. Despite setbacks, China is rapidly advancing its own AI capabilities, striving to reclaim its position as a global leader in this field. This keynote will explore how decoupling influences competition and the risks of creating an imbalanced global development landscape. In particular, the education sector is undergoing a profound transformation due to generative AI. Students increasingly rely on AI to enhance their performance, complicating traditional assessment methods for educational institutions. However, the integration of AR, VR, and AI in teaching presents new opportunities, as demonstrated by large-scale research projects in China that aim to redefine the educational experience (Baxter 2022, Lecqulerc 2023, Günthner 2023). Examples of these innovations will be presented, highlighting their potential impacts on the future of schooling and higher education, ultimately illustrating how technology can reshape learning environments and pedagogical practices (Baxter 2022, Lecqulerc 2023, Günthner 2023). The keynote includes preliminary results of ongoing field work within high school classrooms and Chinese and EU universities, conducted by the EU Jean Monnet Research Centre of Excellence on the Digitalization of University Teaching in China and the EU (2023-2026).

Professor Martin Woesler studied Chinese Studies and early engaged in University Pedagogy Studies and Internet Technology. After his PhD at Bochum University, he was invited for two years to conduct research and to teach extracurricular courses at Harvard University. He held tenure-track and tenured professorship positions in the USA, Germany, Italy, Poland and China. Currently he is Director of the EU Jean Monnet Research Centre of Excellence for the "Digitalization of University Teaching" at Hunan Normal University, China. He conducts research, teaches and publishes in German, English, Chinese (and French). He is Academian of the European Academy of Sciences and Arts, Salzburg, PhD supervisor, Xiaoxiang Scholar, Winner of the 2020 Chinese Friendship Award (Chinese Government), 2023 Friend of Chinese Literature Award (Writers‘ Association), 2024 Outstanding Foreign Translator Award (Translators' Association).

Dr. Jun Fang, Associate Professor

Faculty of Pharmaceutical Sciences, Sojo University, Kumamoto, Japan

Topic: Development of a Polymer Micelle-based Carbon Monoxide Delivery System for Inflammation and Cancer Treatment

View More

2024 9th International Conference on Public Health and Medical Sciences

Aug. 10-12, 2024

Remote sensing technology is an important technical means for human beings to perceive the world, and hyperspectral image classification technology has become the mainstream of current research. Hyperspectral image classification (HSIC) is a pixel-level classification task, which is mainly used for fine extraction and recognition of ground object information. HSIC is the basis for subsequent practical application tasks of hyperspectral images and has very important research significance, which is widely used in digital precision agriculture, environmental monitoring, national defense and military strategy and other fields. With the rapid development of artificial intelligence technology, many new hyperspectral image classification methods and algorithms have been proposed. Moreover, rapid advances in these methods have also promoted the application of associated algorithms and techniques to problems in many related fields. This keynote aims to report and cover the latest advances and trends about the Deep Learning Methods for Hyperspectral Image Classification.

Dr. Jun Fang graduated with a degree in medicine from Bethune Medical University (Jilin University's Faculty of Medicine) in 1995. He completed his master's degree in thoracic surgery at China Medical University in 1998 and then pursued his Ph.D. in Medicine at Kumamoto University's in 2003. From 2003 to 2005, Fang worked as a Research Associate at Duke University Medical Center in the United States. In 2005, he joined Sojo University's Faculty of Pharmaceutical Sciences. Since 2016, he has been a visiting professor at the School of Public Health, Anhui Medical University (China). For many years, he has conducted research on the EPR effect and anticancer and anti-inflammatory nanomedicine. From 2014 to 2017, he was selected as a Highly Cited Researcher in the field of Pharmacology and Toxicology. He was also included in the 2023 global ranking of scholars with Lifetime Academic Influence. In the past decade, he published 130 papers, with a cumulative impact factor (IF) exceeding 700. These papers have been cited over 10,000 times in SCI-source journals, with the highest citation count for a single paper being over 4,800 times.

Dr. Xiaochong Jian, Professor

School of Stomatology, Hainan Medical University, Haikou, China

Topic: Application of Inferior Alveolar Nerve Lateralization in Conjunction with Implant Placement

View More

2024 9th International Conference on Public Health and Medical Sciences

Aug. 10-12, 2024

One of the most common challenges for implant placement is insufficient bone height and volume. Installing implants without encroaching on the inferior alveolar nerve is nearly impossible, especially in patients with atrophic edentulous posterior mandibles. Several treatment options that do not interfere with the nerve canal have been suggested: vertical ridge augmentation, distraction osteogenesis, guided bone regeneration, In more severe cases, another option is inferior alveolar nerve transposition. In contrast, the inferior alveolar nerve transposition can install longer implants and improve the success rate, Reduce the amount of bone grafting and avoid bone removal complications; is simpler and easier than distraction, with lower treatment cost and shorter cycle. The authors made some improvements to the surgical operation, making it easier to master and with fewer complications.

Dr. Xiaochong Jian is a professor at the School of Stomatology, Hainan Medical University. He received his doctorate degree from Hainan Medical University in 2017. He has published more than 10 papers and obtained 26 patents. He has participated in the translation of two professional books and was an editorial board member of oral and maxillofacial surgery. He is good at implant surgery and has implanted more than 10,000 teeth. He often conducts live surgery broadcasts and implant training. He is a senior lecturer for multiple implant brands.

Dr. Jing Du, Professor

Beijing Anding Hospital, Capital Medical University, Beijing, China

Topic: Nuclear Receptors Modulate Inflammasomes in the Pathophysiology and Treatment of Major Depressive Disorder

View More

2024 9th International Conference on Public Health and Medical Sciences

Aug. 10-12, 2024

Major depressive disorder is a common, chronic and recurrent disease. Existing drugs are ineffective to one third of patients, so it is urgent to develop novel and rapid antidepressants. Accumulative evidence has shown that immune inflammation, particularly inflammasome activity, plays an important role in the pathophysiology of MDD. We summarize the evidence on nuclear receptors (NRs), such as glucocorticoid receptor, vitamin D receptor, estrogen receptor, aryl-hydrocarbon receptor, and peroxisome proliferator-activated receptor(PPARs), in modulating the inflammasome activity and depression-associated behaviors. Chronic Social Defeat Stress (CSDS) depressed mice reduced the expression level in prefrontal cortex (PFC) of farnesoid X receptor(FXR), which is a nuclear receptor activated by CDCA. We found that CDCA treatment restored the level of FXR in the CSDS mice, decreased the activity of the NLRP3 inflammasome and caspase-1 and subsequently showed antidepressant effects in the tests of sucrose preference, tail suspension, and forced swimming in CSDS mouse model of depression. Moreover, we also found that ganoderic acid A (GAA) modulated CDCA receptor FXR, inhibited brain inflammatory activity, and showed antidepressant effects in the chronic social defeat stress depression model, tail suspension, forced swimming, and sucrose preference tests. GAA directly inhibited the activity of the NLRP3 inflammasome, and activated the synaptic AMPA by regulating FXR in the PFC of mice. If we knocked out FXR or injected the FXR-specific inhibitor z-gugglesterone (GS), the antidepressant effects induced by GAA were completely abolished. Proteomic analysis identified distinct proteins in CSDS (305), GAA-treated (949), and IMI-treated (289) groups. Enrichment in mitochondrial and synaptic proteins was evident from GO and PPI analyses. PRM analysis revealed significant expression changes in proteins crucial for mitochondrial and synaptic functions (namely, Naa30, Bnip1, Tubgcp4, Atxn3, Carmil1, Nup37, Apoh, Mrpl42, Tprkb, Acbd5, Dcx, Erbb4, Ppp1r2, Fam3c, Rnf112, and Cep41). Western blot validation in the prefrontal cortex showed increased levels of Mrpl42, Dcx, Fam3c, Ppp1r2, Rnf112, and Naa30 following GAA treatment. In another independent study, we found that oridonin significantly enhanced the expression of nuclear receptor PPAR-γ, GluA1 (Ser845) phosphorylation, GluA1 in the total protein extract of the prefrontal cortex (PFC), and showed antidepressant efficacy. Blocking nuclear receptor PPAR-γ was able to block antidepressant effects of oridonin. These studies demonstrate that nuclear receptor signaling regulates neuroimmune and antidepressant behaviors and is potential targets for the treatment of MDD.

Dr. Jing Du is a professor in Beijing Anding Hospital affiliated to Capital Medical University in Beijing, China. In Beijing Anding Hospital, she is engaged in the research of cellular and molecular pathophysiology and neuropsychopharmacology of mental diseases such as depression, anxiety, autism and schizophrenia. She was formerly a staff scientist at the National Institute of Mental Health of the National Institutes of Health(NIH) in USA. She has won many awards, including the NIH Performance Award in Recognition and Appreciation of Special Achievement issued by the National Institutes of Health. She is a Full member of the American College of Neuropsychopharmacology (ACNP). She has published 64 SCI articles in the field of neuropsychopharmacology. Her H index is 35. She is recognized nationally and internationally for her research contributions and achievements in psychopharmacology.

Dr. Jinhui Wang, Associate Professor

Institute of Drug Discovery Technology, Ningbo University, Ningbo, China

Topic: Engineering of Phosphatidylserine-targeting ROS-responsive Polymeric Prodrug for the Repair of Ischemia-reperfusion-induced Acute Kidney Injury

View More

2024 9th International Conference on Public Health and Medical Sciences

Aug. 10-12, 2024

Ischemia-reperfusion-induced acute kidney injury (IR-AKI) commonly occurs in situations such as hemorrhagic shock, kidney transplantation, and cardiovascular surgery. In this study, we developed an ROS-responsive polymeric prodrugs (Zn-D/DTH) which could target the externalized PS of apoptotic cells, and then responsively released HDM (anti-inflammatory peptides) in the presence of intracellular ROS. Zn-D/DTH effectively ameliorated renal function and mitigated pathological alterations such as the loss of the brush border, tubular dilation, and accumulation of cellular debris within the tubular lumens. Furthermore, Zn-D/DTH greatly reduced the generation of pro-inflammatory factors like IL-6, COX-2, and iNOS in renal tissues, suggesting its protective role largely stems from suppression of the inflammatory response. Additional mechanism exploration revealed that Zn-D/DTH markedly decreased the expression levels of TLR4 and MyD88, as well as the phosphorylation of NF-κB in the damaged kidneys. This, in turn, reduced the number of apoptotic tubular cells and the activity of Caspase 9 and Caspase 3 caused by ischemia-reperfusion. Additionally, Zn-D/DTH treatment showed improvement in the long-term renal damage and fibrosis induced by ischemia-reperfusion. The experimental outcomes indicated that Zn-D/DTH attenuated renal ischemia-reperfusion injury and delayed the transition from acute kidney injury to chronic kidney disease by downregulating the TLR4/MyD88/NF-κB signaling pathway and reducing the expression of apoptotic caspases, thereby inhibiting inflammation and reducing cell apoptosis.

Dr. Jinhui Wang is now an associate professor at Ningbo University. In 2014, he was assigned to the University of Toulouse III, France, to pursue a doctorate in metal-organic chemistry and coordination chemistry, under the guidance of Professor Eric Benoist, dedicated to the application of metal-organic complexes in biological imaging research; In 2018, he was introduced to Ningbo University as an outstanding doctor and served as an assistant researcher. His research focuses on Cancer Imaging, Polymer Characterization, Polymerization Nano Drug Delivery and Nanotechnology for Drug Delivery.

Dr. Shou Feng, Associate Professor

College of Information and Communication Engineering, Harbin Engineering University, Harbin, China

Topic: Deep Learning Methods for Hyperspectral Image Classification

View More

2024 10th International Conference on Energy, Environment and Earth Sciences

Aug. 10-12, 2024

Remote sensing technology is an important technical means for human beings to perceive the world, and hyperspectral image classification technology has become the mainstream of current research. Hyperspectral image classification (HSIC) is a pixel-level classification task, which is mainly used for fine extraction and recognition of ground object information. HSIC is the basis for subsequent practical application tasks of hyperspectral images and has very important research significance, which is widely used in digital precision agriculture, environmental monitoring, national defense and military strategy and other fields. With the rapid development of artificial intelligence technology, many new hyperspectral image classification methods and algorithms have been proposed. Moreover, rapid advances in these methods have also promoted the application of associated algorithms and techniques to problems in many related fields. This keynote aims to report and cover the latest advances and trends about the Deep Learning Methods for Hyperspectral Image Classification.

Dr. Shou Feng, Associate Professor, PhD Supervisor of Harbin Engineering University, Deputy Director of the Key Laboratory of Advanced Ship Communication and Information Technology of the Ministry of Industry and Information Technology, IEEE member, Senior member of the Chinese Society of Communications, Member of the Imaging Detection and Perception Committee of the Chinese Society of Image and Graphics, peer review expert of the National Natural Science Foundation of China, academic dissertation review expert of the Ministry of Education, Visiting Scholar of Indiana University Bloomington, Guest Editor of Remote Sensing, an international authoritative journal in the field of remote sensing. Member of the editorial board of international Journal Frontiers in Imaging, American Journal of Remote Sensing, He also serves as a reviewer for many authoritative academic journals such as IEEE TIP, IEEE TGRS, IEEE GRSL, and Remote Sensing. In the past three years, he has published 30 academic papers as the first/corresponding author in top journals in the field of Remote Sensing such as IEEE TIP, IEEE TGRS, and Remote Sensing, and 3 papers have been selected as ESI highly cited papers. As a guest editor, he organized 4 special issues in Remote Sensing, TOP journal of Chinese Academy of Sciences.

Dr. Mingxin Liu, Professor

State Key Laboratory of Applied Organic Chemistry, Lanzhou University, Lanzhou, China

Topic: Tuning 'Green' Feedstock into Value-added C-C Bonds Under Light

View More

2024 10th International Conference on Energy, Environment and Earth Sciences

Aug. 10-12, 2024

The construction of C-C bonds is at the heart of Chemistry as well as sustainable development. The scale and diversity requirement of those processes in modern society is enormous. However, the traditional C-C bond formation process often requires metallic or pre-synthesized 'mediators', which reduces the overall atom economy and generate stoichiometric amount of emissions. We have developed a series of photocatalytic C-C bond formations, granting value-added compounds while using more environmentally-benign reagents, which has further broaderned the utilization of sustainable resource and allows 'green fuels' to be converted into 'green foods/materials'.

Dr. Mingxin Liu is a professor at Lanzhou University. His B.Sc. thesis was fulfilled from Tsinghua University with Prof. Yongge Wei and Prof. Lei Liu. He then finished his PhD at McGill University supervised by Prof. Chao-Jun Li. After finishing his PhD he completed his postdoctoral study with Prof. Zetian Mi from the University of Michigan. He began his independent career as a full professor at Lanzhou University by the end of 2020. His research interest is the application of 'green' reagents in synthetic methodology and their sustainable application in Chemical Biology.

Dr. Rosario Montuori, Professor

Department of Engineering, University of Salerno, Salerno, Italy

Topic: Rational Seismic Design of Steel Moment Resisting Frames Equipped with Dissipative Devices

View More

2024 9th International Conference on Architecture and Urban Planning

Aug. 10-12, 2024

The design of steel structures equipped with energy dissipation devices requires a fractional approach to ensure optimal performance during seismic events. In the event of an earthquake, the dissipative devices integrated within the structure must activate precisely, with all and only the designated dissipators engaging in plastic excursions. Meanwhile, the remaining portion of the structure must remain within the elastic range, ensuring that all non-dissipative members remain undamaged. This approach to design aligns perfectly with the principles of capacity design, which are fundamental for ensuring structural resilience under seismic loading conditions. However, many modern design codes only address these criteria from a partial rather than a global perspective. This presentation will explore the importance of adopting a fractional design approach to effectively integrate energy dissipation devices into steel structures while maintaining overall structural integrity and seismic performance. Additionally, the keynote will provide a clear overview of simple and rational design procedures capable of activating only the designated dissipative zones during collapse. These procedures ensure that energy dissipation devices integrated into the structure engage precisely as intended, effectively dissipating seismic energy input. Several simple examples will be shown and discussed.

Dr. Rosario Montuori is full Professor of Structural Engineering at the University of Salerno, Italy. He received a M.S/B.S. in Civil Engineering from the University of Salerno in 1997 and the PhD in Structural Engineering from the same University in 2001. He is author of more than 150 journal articles, conference papers and scientific reports. His principal research activity is devoted to the control of the collapse mechanism for steel structures by means of a rigorous application of “capacity design”. In particular, the research activity concerns the following structural typologies: Concrete Moment Resisting Frames, Steel Moment Resisting Frames with semi-rigid joints, Steel Irregular Moment Resisting Frames, Concentrically “X” and “V” Braced Frames, Concentrically “X” Braced Frames with Reduced Section (based on the reduction of the cross section area at the ends of the bracing members aiming to calibrate the axial resistance to a value equal to the internal action occurring under seismic load combination), Eccentrically Braced Frames, Moment Resisting Frame-Concentrically Braced Frame dual systems and Truss Moment Frames with special devices located at the bottom chord level at the ends of the truss girders. For several of the considered structural typologies, also the seismic structural reliability defined as the mean annual frequency (MAF) of exceeding a threshold level of damage, i.e. a limit state has been investigated and compared with reference both to the proposed design methodologies and to EC8 provisions. He developed theoretical fiber models able to predict the Moment-curvature behaviour of RC columns confined by means of angles and battens and of Concrete Filled Steel Tubular Columns (CFT) with Square Hollow Section (SHS). The proposed models have been validated by means of experimental tests. He developed a design procedure for some Tensegrity Structures able to account both for local and global stability in order to find the optimal design of minimum mass. He has participated as research staff member in various research projects funded by the Italian Ministry of Education and the Italian Network of Seismic Engineering Laboratories (ReLUIS).

Dr. Lanhui Guo, Professor

School of Civil Engineering, Harbin Institute of Technology, Harbin

Topic: Research and Application of Buckling Restrained Steel Plate Shear Walls

View More

2024 9th International Conference on Architecture and Urban Planning

Aug. 10-12, 2024

In this study, a novel type of coupled steel tubes is proposed and utilized as buckling restraining members in BRSPSWs. These coupled steel tubes offer advantages of lightweight construction and ease of assembly. The transition from partial to full constraint of the steel plate can be achieved by adjusting the number of coupled steel tubes. Quasi-static cyclic loading tests are conducted on specimens including unstiffened, partially buckling restrained, and fully buckling restrained SPSWs. The in-plane mechanical properties and out-of-plane interaction behavior are obtained. simplified models of BRSPSW are established to analyze interaction behavior between steel plate and buckling restraining members. The working mechanism is clarified and formulas that unify the calculation of out-of-plane interaction force for both the fully and partially BRSPSWs are proposed. Finite element models are established and validated using the test results. Parametrical studies are conducted to investigated the effect of key parameters on the in-plane and out-of-plane performance of BRSPSW. Finally, the experiment and FE results are applied to validate the accuracy of the proposed formulas. Necessary modification factors are derived and simplified design method of the out-of-plane buckling restraining members is proposed.

Dr. Lanhui Guo is a professor at Harbin Institute of Technology, China. Prof. Guo has published over 100 internationally renowned SCI journal papers with over 3000 citations. He has been ranked in the top 2% of global scientists at Stanford University in 2023. He is the secretary-general of the China Steel Construction Society Association for Steel-Concrete Composite Structures. He is also the associate editor of the International Journal of Steel Structures. His research work is focused on the advanced composite members and composite structures. He presides over 30 projects including the National Natural Science Foundation of China, the Province of Heilongjiang's outstanding youth science fund, etc. He won the first prize of Heilongjiang Provincial Science and Technology Progress, and the first prize of the Science and Technology Progress, China Steel Construction Society.

Dr. Mohammad Ali Moradi, Associate Professor

Faculty of Entrepreneurship, University of Tehran, Tehran, Iran

Topic: Exploring Institutional Enablers in the Entrepreneurial Ecosystem: An Integrated Framework for Nanotechnology

View More

2024 9th International Conference on Economics, Management and Social Sciences

Aug. 10-12, 2024

Recent scholarship has increasingly acknowledged the vital role of institutions in nurturing entrepreneurial activities and fostering entrepreneurial ecosystems. However, existing literature lacks a comprehensive exploration of the diverse institutional enablers and their policy implications that influence entrepreneurial ecosystems and impact entrepreneurial ventures. Addressing this gap, this study adopts an institutional perspective to investigate the institutional enablers that facilitate nanotechnology business ventures and identifies mechanisms and key policy areas that can enhance the entrepreneurial ecosystem in Iran, an emerging economy. The study involved 23 semi-structured interviews with policymakers and stakeholders in the technology and nanotechnology sectors. Through rigorous thematic analysis, four main institutional enablers were identified: state-driven initiatives, non-governmental institutions, institutional entrepreneurship, and international institutions. These entities influence the entrepreneurial landscape through regulatory frameworks, policy collaborations, and shaping the overall ecosystem. The findings underscore the significant role of formal institutions in shaping the entrepreneurial environment within the nanotechnology sector. The study proposes four propositions concerning institutional enablers and their policy frameworks, offering insights into how these institutions can foster a conducive environment for entrepreneurial activities in nanotechnology.

Dr. Mohammad Ali Moradi is an associate professor at the University of Tehran's Faculty of Entrepreneurship, currently serving as a visiting associate professor at Peking University HSBC Business School. With a PhD from the University of Liverpool, his research interests span entrepreneurial finance, business venture creation, business model innovation, business environment and governance, and entrepreneurship development policies. He has authored two books and published extensively, including articles in national and international journals and encyclopedias. His book "Business Environment: Theories, indices, and techniques" was honored as Book of the Year among university publishers in Iran in 2016. Recognized for his contributions, he has received accolades such as top professor at the University of Tehran in 2016 and awards for supervising outstanding MA and PhD dissertations in growth and employment topics in Iran. His academic achievements were further acknowledged by the Ministry of Science, Research and Technology of Iran in 2009. Moradi has served as a referee for various journals, taught PhD and MA courses, supervised 46 students, and conducted workshops on entrepreneurship development policymaking, macroeconomics, and business environment analysis.

Dr. Zhuming Chen, Professor

School of Business, Sun Yat-sen University, Guangzhou, China

Topic: Optimal Timing and Conditions for Forming a FinTech Institution (FTI)

View More

2024 9th International Conference on Economics, Management and Social Sciences

Aug. 10-12, 2024

This study analyzes the optimal timing and conditions for forming a FinTech institution（FTI) using real options game theory. Three typical ways of forming FTIs are presented : investment, the merging of a traditional financial institution (TFI) with a digital technology company (DTC), and the merging of a DTC with a TFI. Our results indicate that higher financial license values, faster financial industry growth, and higher required returns by shareholders, all contribute to accelerating the formation of FTIs. Additionally, stronger correlations between upstream and downstream products and higher FinTech index factors further expedite this process. Conversely, higher financial industry volatility and transaction costs slow this process down. This method can also be applied to study other topics, such as general vertical mergers and the digital transformation of industry.

Dr. Zhuming Chen is a Professor and doctoral supervisor in finance at the School of Business, Sun Yat-sen University. A member for the Game Theory Society. Vice Chairman of the Risk Investment Professional Committee of the 9th China Management Annual Conference; Member of Guangdong Green Finance Expert Committee, Member of Guangdong Digital Government Construction Expert Committee, and Director of Guangdong Economic Society. His research interests include financial investment; venture capital; FinTech; risk management of Banks. More than 50 research articles and case studies have been published in high-level journals such as International Review of Economics and Finance (SSCI), North American Journal of Economics and Finance (SSCI), Journal of Financial Research (SSCI), International Journal of Information Technology & Decision Making (SSCI&SCI), Computational Economics (SCI), Journal of Systems Science and Systems Engineering (SCI), Journal of Emerging Markets [J], Financial Innovation (SSCI), as well as in domestic and international journals such as "China Management Science" and "Research on Quantitative Economics and Technological Economics". Research papers have been presented at MFA 2012 meetings and other domestic and international academic conferences. Cover Person of Contemporary Economy, Issue 12, 2013.

Dr. Ting Wei, Associate Professor

School of Physical Education, Inner Mongolia University, Hohhot, China

Topic: Research Progress of Skeletal Muscle Electromyography Technology in the Field of Sports Medicine

View More

2024 8th International Conference on Health, Medicine and Life Sciences

Jun. 1-2, 2024

With the leap of skeletal muscle surface electromyography technology, the advantage of observing the activity characteristics of motor units through skeletal muscle surface electromyography technology has become a core issue in academia. In order to grasp the accuracy and research progress of skeletal muscle electromyography technology, the research results show that skeletal muscle electromyography technology is mainly used to study neuromuscular recruitment characteristics, which can predict muscle strength and recognize movements; Single joint muscle strength training can induce a decrease in motor unit recruitment threshold and an increase in discharge frequency, resulting in an increase in muscle strength; Meat control function; Muscle fatigue induced by exercise leads to a decrease in the discharge frequency of the motor unit, altering the relationship between recruitment threshold and discharge frequency, recruitment threshold and exit threshold, as well as the balance and excitability of the motor unit's activity in different electromyographic frequency bands.

Dr. Ting Wei is an associate professor at the School of Physical Education of Inner Mongolia University. She received a Ph.D degree from Beijing Sport University in 2020 and was selected for the Inner Mongolia Autonomous Region Talent Project. She is the director of the laboratory at the School of Physical Education of Inner Mongolia University, the secretary of the Party branch of faculty and staff, a mentor of the National College Student Innovation and Entrepreneurship Competition, and a graduation thesis review expert. Published over 60 papers in core journals, sports special issues, and important academic conferences both domestically and internationally.

Dr. Weihong He, Associate Professor

Department of Physiology, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, China

Topic: Inhibiting RUNX1 Leads to Reduced Infarct Size and Repressed Cardiac Cathepsin Levels Following Acute Myocardial Infarction

View More

2024 8th International Conference on Health, Medicine and Life Sciences

Jun. 1-2, 2024

Acute myocardial infarction (MI) is a leading cause of death worldwide. Acute MI results in prolonged myocardial ischemia and the subsequent cell death leads to heart failure which is linked with increased deaths or hospitalizations. Cathepsins are lysosomal proteases involved in protein degradation and can also be secreted into extracellular spaces. Recent evidence has shown that cardiac release of a subtype of cathepsin (cathepsin-L) in MI patients leads to elevated serum cathepsin-L levels which are associated with reduced cardiac function and increased infarct size. However, the mechanism of the increased cathepsin-L level is unknown. Runt-related transcription factor-1 (RUNX1) is a master-regulator transcription factor, which is implicated in the transcriptional regulation of gene expression. Recent evidence demonstrated that RUNX1 plays a critical role in the heart after MI. This work sought to investigate whether inhibition of RUNX1 affects cathepsin levels in a rat MI model. MI was surgically induced by performing coronary artery ligation. Heart samples were taken at 24 hours post-MI and analyzed by LC-MS/MS operating in the data-independent acquisition (DIA) mode. We found that overall cathepsin levels were increased in control hearts after MI. In contrast, rats treated with RUNX1 inhibitors demonstrated decreased cathepsin levels. Furthermore, RUNX1 inhibition led to a reduced infarct size at 24 hours post-MI as determined through 2,3,5-triphenyltetrazolium chloride (TTC) staining. These results are in line with Dr. He’s previous study performed in isolated rat hearts which demonstrates that inhibition of cathepsin-L reduces infarct size and improves cardiac function ex vivo. The present study shows that inhibition of RUNX1 after acute MI can also reduce infarct size in rat hearts in vivo and the beneficial effects may be achieved by repressed cathepsin levels, thus suggesting the translational potential of RUNX1 and cathepsins as therapeutic targets of cardiac protection against acute MI.

Dr. Weihong He is a principal investigator and Associate Professor at the Department of Physiology, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University. Dr. Weihong He obtained an MD (2012) at West China School of Medicine & West China Hospital, Sichuan University, and completed a PhD (2017) at the BHF Glasgow Cardiovascular Research Centre, University of Glasgow. He was an associate professor at Jining Medical University (2018-2020). Since 2020, he has led a research group to study the pathophysiology of cardiovascular diseases and to investigate novel therapeutic drugs for myocardial infarction and cerebral infarction at Sichuan University. He also teaches physiology and mentors both national and international students. He has expertise in many methodologies which span the level of biochemistry, cell biology, isolated heart, and whole animal in vivo disease models.

Dr. Dongyang Jiang, Research Professor

School of Medicine, Tongji University, Shanghai, China

Topic: Multifaceted Roles of PHACTR1 in Atherosclerosis

View More

2024 9th International Conference on Biology and Life Sciences

Jun. 1-2, 2024

Although compelling evidence suggested the strong association of phosphatase and actin regulator 1 (PHACTR1) with atherosclerosis, the biological function of PHACTR1 remains poorly understood. Macrophage PHACTR1 was demonstrated to be protective by facilitating efferocytosis and attenuating atherosclerotic plaque necrosis. Our group identified the pro-atherosclerotic effect of endothelial PHACTR1, contrary to macrophage PHACTR1. Global or endothelial cell (EC)-specific PHACTR1 deficiency significantly inhibited atherosclerosis in regions of disturbed flow. PHACTR1 was enriched in ECs and located in the nucleus of disturbed flow area but shuttled to cytoplasm under laminar flow in vitro. RNA-seq using EC-enriched RNA showed that Phactr1 depletion affected vascular function and peroxisome proliferator-activated receptor gamma (PPARγ) was the top transcription factor regulating differentially expressed genes. PHACTR1 functioned as a PPARγ transcriptional corepressor by binding to PPARγ through the corepressor motifs. PPARγ activation protects against atherosclerosis by inhibiting endothelial activation. Consistently, PHACTR1 deficiency remarkably reduced endothelial activation. PPARγ antagonist GW9662 abolished the effects of Phactr1 knockout on EC activation and atherosclerosis. Targeting the interaction of PHACTR1 and PPARγ will provide a promising therapeutic strategy for atherosclerosis.

Dr. Dongyang Jiang is a research professor at Department of Cardiology, Pan-vascular Research Institute, Shanghai Tenth People's Hospital, Tongji University School of Medicine. She earned her bachelor's (basic medical science) and doctoral (immunology) degrees at Peking University and then joined Tongji University in 2014. She is the principal investigator for general and youth projects of the National Natural Science Foundation of China, a general project of the China Postdoctoral Science Foundation and a project of the Shanghai Science and Technology Committee. She also serves as the member of the Chinese Society of Immunology (CSI), the Chinese Society of Biochemistry and Molecular Biology (CSBMB), and the American Heart Association (AHA). Her research focuses on the molecular mechanisms of dynamic regulation of the endothelial barrier, exploring the roles in cardiovascular diseases such as atherosclerosis, abdominal aortic aneurysm, and pulmonary arterial hypertension. Her team recently discovered a novel mechanosensitive molecule in atherosclerosis, PHACTR1. She has published 15 research articles in scholarly journals including ATVB, Hypertension, Cancer Letters, and the British Journal of Pharmacology. She has been honored with awards such as AHA Young Scientist Travel Award and CSBMB Young Scientist Award.

Dr. Jun Liu, Professor

School of Mechanical and Electrical Engineering, Southwest Petroleum University, Chengdu, China

Topic: Failure Forms and Safety Evaluation Methods of Downhole Tools in Horizontal Well Development

View More

2024 8th International Conference on Energy, Environment and Resources

Jun. 1-2, 2024

Horizontal well is a development technology that increases oil well production and enhances the economic benefits of oilfield development by expanding the drainage area of the oil reservoir. More and more oil&gas fields are being developed horizontally. The integrity of downhole tools, including tubing and casing, is a prerequisite for achieving horizontal well development. Due to the influence of complex wellbore structures and complex mining conditions such as faults, high temperature and pressure, the mechanical condition of the tubing and tools during drilling, completion, and production is relatively poor, and the failure mechanism is complex. In the speech, we introduce the failure forms, research progress, and urgent problems of pipe columns and downhole tools in horizontal well development.

Dr. Jun Liu graduated with a doctoral degree in Engineering Mechanics from Chongqing University in 2011. He is currently engaged in teaching and research work in the Department of Mechanical Engineering at Southwest Petroleum University, with a research interest in mechanical analysis and safety evaluation of oil and gas well tubing. He has published over 40 SCI papers, 2 monographs, 18 authorized invention patents, and more than 10 provincial and ministerial level scientific research awards.

Dr. Qiming Huang, Associate Professor

College of Safety and Environmental Engineering, Shandong University of Science and Technology, Qingdao, China

Topic: Key Technologies for Dust Reduction at the Source of Deep Coal Seam Water Injection with Strong Permeability and Increased Lubrication

View More

2024 8th International Conference on Energy, Environment and Resources

Jun. 1-2, 2024

Coal, as a fundamental energy and industrial raw material in China, has long provided strong support for economic and social development. However, the coal mining industry is also a high-risk industry with frequent accidents. The 14th Five Year Plan for Mine Safety Production emphasizes that coal mine production should adhere to safe development, source control, and precise prevention and control. Coal seam water injection is an effective technical means to achieve the source control of dust disasters. However, compared to Western coal producing countries such as the United States and Australia, China's coal seam permeability is generally low, which seriously restricts the effectiveness of coal seam hydraulic disaster prevention. Traditional coal seam water injection technology cannot effectively solve such problems, and it is necessary to develop efficient, accurate, and reliable key technologies for coal seam hydraulic disaster prevention. In response to the engineering problems of poor permeability and difficulty in water injection in deep coal seams in China, the principle of "automation and integration" was first integrated into the research and development of dust reduction technology and equipment at the source of low-permeability water injection. Based on a large amount of laboratory testing and on-site industrial test data, a key parameter optimization algorithm for strong permeability and increased lubrication in deep coal seams was established. Based on the physical and chemical properties of low-permeability coal seams, a series of materials for clean and composite efficiency enhancement of coal seam water injection were developed from two aspects: crack structure transformation and interface wetting optimization. By combining hydraulic slotting with high-pressure water injection technology, a key technology of hydraulic strong infiltration and lubrication integration has been proposed, forming a systematic analysis and judgment method for key technical parameters. A coal seam hydraulic strong infiltration and lubrication integration technology equipment has been developed and successfully applied in coal seam water injection dust reduction projects in typical disaster mining areas in China.

Dr. Qiming Huang is an associate professor at Shandong University of Science and Technology, serving as the scientific editor of the Journal of Coal (English version) and a young editorial board member of Coal Geology and Exploration and Metal Mines. Hosted one National Natural Science Foundation Youth Fund project, one China Postdoctoral Science Foundation general funding project, one Shandong Province Postdoctoral Innovation project, one open project of the Key Laboratory of Industrial Dust Prevention and Occupational Safety and Health Education of the Ministry of Education, and four horizontal projects commissioned by enterprises. In the past 5 years, he has published 20 SCI papers as the first or corresponding author, and has been granted 12 invention patents and 5 utility model patents for the first time; The first published monograph titled "The Mechanism of Influence of Water-based Fracturing Fluid on Coalbed Methane Flow". The research work has won the second prize of Chongqing Science and Technology Progress Award and the first prize of China Occupational Safety and Health Association Science and Technology Award.

Dr. Shunli Wang, Professor

School of Electric Power, Inner Mongolia University of Technology, Hohhot, China; Smart Energy Storage Institute, China

Topic: Core State Factor Monitoring of Smart Energy Storage Systems

View More

2024 8th International Conference on Energy, Environment and Resources

Jun. 1-2, 2024

As an important component of the smart grid energy storage system, high-precision state of health estimation of lithium-ion batteries is crucial for ensuring the power quality and supply capacity of the smart grid. To achieve this goal, an improved integrated algorithm based on multiple layer kernel extreme learning machine and genetic particle swarm optimization algorithm is proposed to estimate the SOH of Lithium-ion batteries. Kernel function parameters are used to simulate the update of particle position and speed, and genetic algorithm is introduced to select, cross and mutate particles. The improved particle swarm optimization is used to optimize the extreme value to improve prediction accuracy and model stability. The cycle data of different specifications of LIB units are processed to construct the traditional high-dimensional health feature dataset and the low-dimensional fusion feature dataset, and each version of ML-ELM network is trained and tested separately. The numerical analysis of the prediction results shows that the root mean square error of the comprehensive algorithm for SOH estimation is controlled within 0.66%. The results of the multi-indicator comparison show that the proposed algorithm can track the true value stably and accurately with satisfactory high accuracy and strong robustness, providing guarantees for the efficient and stable operation of the smart grid.

Dr. Shunli Wang is a Professor, Doctoral Supervisor, Executive Vice President of Smart Energy Storage Institute, Academic Dean of Electric Power College at Inner Mongolia University of Technology, Academician of Russian Academy of Natural Sciences, IET Fellow, Provincial Senior Overseas Talent, Academic Leader of the National Electrical Safety and Quality Testing Center, Tianfu Qingcheng Provincial Scientific and Technological Talent, Academic and Technical Leader of China Science and Technology City, Top 2% Worldwide Scientist. His research interests include modeling, state estimation, and safety management for energy storage systems. 56 projects have been undertaken, supported by National Natural Science Foundation of China and the Provincial Science and Technology Department et al. 258 research papers have been published with RIS value of 11617 and h-index value of 29. 52 intellectual property rights have been approved. 9 monographs have been published by famous publishers of Elsevier and IET and so on. The total print number of New Energy Technology and Power Management reaches 6300 copies that are reprinted 4 times. He has guided students on 29 science and technology innovation projects with 6 excellent completion and 34 awards in science and technology competitions. He has won 13 scientific and technological awards, including the Gold Award at the 48th Geneva Invention Exhibition.

Dr. Xueyong Tian, Professor

School of Environmental and Chemical Engineering, Shenyang University of Technology, Shenyang, China

Topic: Modeling and Optimization of Wastewater Treatment Process Based on Artificial Intelligence Technology

View More

2024 8th International Conference on Energy, Environment and Resources

Jun. 1-2, 2024

With the development of the economy and the deepening and strict requirements of the dual carbon goals, new technological challenges have been proposed for sewage treatment processes and processes. This requires the combination of new technologies and methods in sewage treatment processes to expand both theory and application. The rapid development of artificial intelligence technology has brought new growth momentum to the field of water treatment. The sewage treatment process involves complex chemical, physical, and biochemical processes, making it difficult to establish accurate mathematical models. The traditional ASM model series applied to activated sludge processes is an approximate model under many constraints, and its scope of use is limited. The widely used membrane technology lacks relevant model-based analysis. Many key parameters and indicators in the sewage treatment process lack real-time and accurate measurement techniques, resulting in delayed measurement data and affecting precise process control. Artificial intelligence technology can utilize historical, real-time data, and other algorithms such as machine learning, neural networks, and deep learning to achieve process modeling, soft measurement of key parameters and indicators, process control, and optimization of many sewage treatment processes, thereby helping to solve new challenges encountered in sewage treatment.

Dr. Xueyong Tian was born in Linyi, Shandong Province, China in 1979. He received the Ph.D. degree in 2011. He is currently a professor and the director of the Institute of Intelligent Environmental Protection Technology at Shenyang University of Technology, member of the Environmental Perception and Protection Automation Professional Committee of the Chinese Society of Automation. He has led sub projects of the National Key R&D Program, key R&D projects in Liaoning Province, and key scientific and technological research projects in Liaoning Province, and has led more than ten achievement transformation projects. He has participated in more than ten major projects, including the National 973 Project, the National 863 Project, and the Military Key Equipment Reform and Promotion Project. He has published more than 30 papers and obtained more than 20 patents and software copyrights.

Dr. Weiwei Wu, Professor

School of Management, Harbin Institute of Technology, Harbin, China

Topic: How Do Latecomer Firms Achieve Catch-up Through Technology Management: A Comparative Analysis

View More

2024 7th International Conference on Innovation Management and Entrepreneurship

May 31-Jun. 1, 2024

The catch-up of latecomer firms has been a topic of interest because it is closely related to the changes in industry leadership. The reason why some countries are more successful in catch-up is because of their increasing mastery of technology management (TM). Therefore, to ensure successful catch-up, it is imperative for latecomer firms to understand the TM practices and TM modes across national boundaries. This paper aims to reveal the differences in TM practices and TM modes between latecomer firms and forerunner firms. This paper collected data from Chinese firms and Korean firms as latecomers and forerunners, respectively, to examine the differences in TM practices and TM modes. The results show that latecomer firms place more emphasis on grasping the condition of firms’ equipment, understanding technology talents required by business, and completing files on technology information. While forerunner firms stress learning from other competitors, effective training, and constructing detailed technology information management system most. Furthermore, the relationship between TM and product innovation performance is more integrated for forerunner firms compared to latecomer firms. A key contribution of this paper is to reveal the differences in TM practices and TM modes between latecomer firms and forerunner firms, which enriches the catch-up literature from an international comparative perspective. As such, this paper is of great importance in broadening the understanding of how latecomer firms transform into global leaders.

Dr. Weiwei Wu is a Professor at Harbin Institute of Technology, China, where he received his Ph.D, master’s and bachelor’s degrees in management. He was a SPURS (Special Program for Urban and Regional Studies) fellow from 2011 to 2012 at Massachusetts Institute of Technology, USA, and served as the consultant of Asian Development Bank (ADB) from 2021 to 2024. His research focuses on technology management and technological innovation. His research has been supported by the National Natural Science Foundation of China, National Social Science Foundation of China, Ministry of Education of China and etc. He has published more than 100 articles in journals, including Information & Management, Journal of Manufacturing Technology Management, Telematics and Informatics, Journal of Knowledge Management, among others. He serves as the Associate Editor of Journal of Management Science. He is the Council Member of Chinese Society of Technology Economics, and the Founding Member of Asia Entrepreneurship Education Association.

Dr. Jian Chen, Associate Professor

Belt and Road School, Beijing Normal University, Beijing, China

Topic: Analysis on China’s Investment Distribution and Risk Prevention of "the Belt and Road" Countries

View More

2024 8th International Conference on Economics, Finance and Management Science

May 31-Jun. 1, 2024

The study first analyzes the necessity of country risk assessment for "the Belt and Road" investment. Then the author presents the investment distribution characteristics of "the Belt and Road" countries, thus builds up "the Belt and Road" investment country risk evaluation index system with 5 dimensions and 25 indicators and Evaluation model. At last, the authors concludes with some dynamic characteristics of national investment and risk prevention strategies.

Dr. Jian Chen is an Associate Professor at Belt and Road School, and International Business Faculty, Beijing Normal University, Visiting Scholar at De Montfort University (UK) Business School(2004), Visiting Scholar at Gloucestershire University (UK) (2006.9-2007,2), Visiting Scholar(2016.3-2017.3) supported by the Chinese National Scholarship Council(CSC), Research Fellow in the Institute of Energy and Sustainable Development and the School of Business and Law, De Montfort University (UK). She received her doctorate degree in Management from Huazhong Agricultural University and her master degree from Wuhan University. She has participated in 4 international projects, 8 provincial projects, chaired 10 city projects, 7 school-level projects; 1 social science project in Hubei Province, and 2nd excellent economic and social development research project in Meizhou City Awards, Guangdong Province, presided over the University’s Teaching Quality Project of the school-level by the courses of: "Organizational Behavior", "Principles of Management", "Enterprise Strategic Management". She has published 91 academic papers, including 3SCI journals,4 CSSCI journals, 3 ISTP papers, and 4 core journals of CSTPCD, with 2 works, 1 textbook, and 3 joints.

Dr. Peiwen Bai, Professor

School of Economics, Xiamen University, Xiamen, China

Topic: Digital Economy Development and Markup of Firm

View More

2024 8th International Conference on Economics, Finance and Management Science

May 31-Jun. 1, 2024

The integration of the digital economy with the real economy is a major issue for sustainable economic development in the new era. A large amount of economics literature focuses on the benefits of digital economy, such as increased productivity, increased innovation and improved organizational structure of firms. However, little literature has explored the costs of digital economy development to firms, such as increased competition and rising cost pressures. In this paper, we use the markup of firms as a combined reflection of the net value of these two effects, and theoretically construct a variable markup model to explore the mechanism. Further, this paper uses data on Chinese industrial firms from 2004-2013, combined with the newly developed imperfect instrumental variables method. It is found that digital economy development significantly reduces markup of firms, with a robust upper bound of -0.31%. The analysis of the mechanism reveals that the increase in competition among firms and the imperfect transfer of cost pressures by firms are important channels. The heterogeneity analysis shows that the negative impact of digital economy development on markup is larger for firms with characteristics such as less digitalization, tighter financing constraints, and weaker cost-saving capabilities. The study also finds that digital economy development reduces the dispersion of markup and significantly improves the efficiency of resource allocation. The findings of this paper have important implications for understanding the significance of digital economy and real economy integration, and how to further promote digital economy development effectively.

Dr. Peiwen Bai currently works as a professor and doctoral supervisor at the School of Economics of Xiamen University, deputy director of the Economic Research Institute, deputy editor of "China Economic Issues", visiting scholar at Monash University, chief expert of major projects of the National Social Science Fund, and vice president of the Fujian Economic Association. He received a doctorate in management from Shanghai Jiao Tong University in 2006. His research areas are the digital economy and income distribution. He has published over 80 papers as first author in domestic and foreign journals. He has participated in and chaired research on multiple projects, including the National Social Science Fund Major Project and the Ministry of Education Major Project.