Exploring Science Academic Conference Series
The Conference Proceedings Series publishes peer-reviewed papers presented at the Exploring Science conferences. These proceedings are a valuable resource for the scientific community, providing access to the latest research findings, innovative methodologies, and interdisciplinary studies.
LTβR Agonist and IFN-I Cooperatively Induce Antitumor Tertiary Lymphoid Structure Formation: Mechanisms and Therapeutic Prospects
This review systematically explores the antitumor strategy of combining lymphoxin beta receptor agonists with type I interferons to induce tertiary lymphoid structure formation, thereby transforming “cold” tumors into “hot” tumors. The article pointed out that this combination therapy can synergistically overcome the microenvironmental barriers of “cold” tumors: LTβR agonists provide structural scaffolds for the formation of tertiary lymphoid structures by activating tumor stromal cells, especially inducing high endothelial vein generation to promote lymphocyte homing; Type I interferon, on the other hand, provides powerful immune activation signals through its downstream signaling pathway, including promoting the recruitment and activation of B cells and T cells, enhancing the function of antigen-presenting cells, and driving B cells to differentiate into follicular-like B cells. Preclinical studies have confirmed that this strategy can effectively enhance the efficacy of existing therapies such as immune checkpoint inhibitors and chimeric antigen receptor T cells, and induce durable anti-tumor immune memory in neoadjuvant therapy, showing broad clinical translation prospects.
tertiary lymphoid structures, LTβR agonist, type I interferon, tumor immunotherapy
Copyright (c) 2026 Xiaoyin Wang (Author) https://creativecommons.org/licenses/by/4.0
Modeling and Fault Analysis of Complex Mechatronic Systems Based on MBSE
As high-end equipment evolves towards higher complexity and multi-disciplinary coupling, the interaction among mechanical, hydraulic, and control systems becomes increasingly close. The traditional document-based systems engineering (DBSE) method often encounters bottlenecks, such as fragmented information, poor traceability, and a disconnect between design and analysis, when dealing with the “logical-physical” coupling relationships of complex systems. Therefore, a new methodology capable of uniformly expressing the characteristics of multiple domains is urgently needed. This paper aims to systematically review the research progress in model-based systems engineering (MBSE) for modeling and fault analysis of complex mechanical-electro-hydraulic systems, summarize the current application status of MBSE in overcoming disciplinary barriers and achieving full life-cycle management, and provide references for engineering practice in related fields. Based on mainstream MBSE design processes such as MOFLP-R, and by integrating relevant domestic and foreign literature, this paper has conducted an induction and review from three dimensions: theoretical framework, modeling technology, and application cases. The focus is on analyzing the integration mechanism of multi-domain physical modeling languages such as SysML and Modelica, as well as the application models of MBSE in typical industries such as aviation and rail transit. Existing studies have shown that MBSE effectively achieves closed-loop traceability from requirements to physical realization by establishing a unified system architecture model. In fault analysis, model-based fault injection and joint simulation technologies have been proven to improve early fault prediction capabilities and significantly reduce R&D costs. However, current research still lacks interoperability standards for models, the development of interdisciplinary talent teams, and intelligent modeling empowered by AI. MBSE is an effective paradigm for solving the design and verification problems of complex mechanical-electro-hydraulic systems. Future research should focus on deep integration of MBSE with digital twins and artificial intelligence, and on the construction of standardized collaborative environments to promote the digital transformation of high-end equipment development models.
model-based systems engineering (MBSE), review, fault analysis, mechanical-electro-hydraulic system, multi-domain modeling
Copyright (c) 2026 Sichen Yao (Author) https://creativecommons.org/licenses/by/4.0
From Mathematical Elegance to Empirical Challenges: A Critical Review of Modern String Theory
String theory remains one of the most compelling theoretical frameworks for reconciling general relativity with quantum mechanics and unifying the four fundamental interactions. This review critically examines the theoretical foundations of string theory—including its postulation of one-dimensional extended objects, supersymmetry, extra dimensions, and D-branes—and evaluates its major applications in quantum gravity, black hole thermodynamics, and early-universe cosmology. While string theory has achieved remarkable mathematical triumphs, most notably through the AdS/CFT correspondence and the microscopic derivation of black hole entropy, it currently faces profound empirical challenges. The absence of low-energy supersymmetry at the Large Hadron Collider (LHC) and the vast unpredictability of the string landscape heavily constrain its phenomenological viability. By juxtaposing its rigorous theoretical successes against the severe lack of direct experimental evidence, this article provides a balanced and critical perspective on the current status, fundamental limitations, and prospects of string theory.
string theory, unification, quantum gravity, D-branes, dualities
Copyright (c) 2026 Yuheng Jackson Qian (Author) https://creativecommons.org/licenses/by/4.0
House Price and Consumption Inequality in China: Micro Evidence from CFPS
Based on microdata from the China Family Panel Studies (CFPS) from 2010 to 2022, this paper constructs the Kakwani index to measure household consumption relative deprivation and uses a high-dimensional fixed-effects model to examine the impact of housing prices on consumption inequality. The results show that rising housing prices significantly aggravate consumption inequality, and this conclusion remains stable after a series of robustness tests. Heterogeneity analysis indicates that the unequal effect of housing prices is prominent in eastern China and the elderly group, but is not significant or even negative in central and western regions and young groups. This paper theoretically explains the transmission channels from the perspectives of wealth effect, purchase budget constraint and spatial differentiation, but does not conduct empirical mechanism tests. The conclusions provide micro evidence for the formulation of differentiated real estate regulation and consumption equity policies.
housing prices, consumption inequality, Kakwani index
Copyright (c) 2026 Yiyang Liu (Author) https://creativecommons.org/licenses/by/4.0
The Complementary Mechanisms and Collaborative Governance Construction between Street Vendors and Formal Community Commerce
China’s urbanization has entered a new stage characterized by high-quality development and refined governance. Street vendors and community-based commerce coexist as integral components of livelihood services. From the perspective of functional complementarity, this paper first analyzes the characteristics of street vendors and formal community commerce in terms of service positioning, spatiotemporal layout, business structure, and demand responsiveness. Second, it examines the differences and complementarities in their commercial functions, identifying complementarities in time, product categories, pricing, and goods provision. Finally, by clarifying the dimensions of complementarity and the interaction logic between the two within the community commercial ecosystem, the paper proposes a framework for their collaborative governance. This study provides a reference for balancing urban order, livelihood security, and community vitality, and promotes the formation of a new pattern of community commerce that is convenient, orderly, and dynamic, characterized by “formal commerce as the mainstay and street vendors as a supplement.”
street vendors, community-based commerce, functional complementarity, commercial ecosystem, informal economy, urbanization
Copyright (c) 2026 Yuanjing Wang (Author) https://creativecommons.org/licenses/by/4.0
Measurement and Coupling Relationship of Economic Resilience in Southwest China Driven by the Digital Economy
Under the dual background of the advancement of the “Digital China” strategy and the increasing global economic uncertainty, the synergistic development of the digital economy and regional economic resilience has become a key approach to addressing regional development imbalances and enhancing risk resistance capabilities. This study takes the four southwestern provinces and cities, Sichuan, Chongqing, Yunnan, and Guizhou, as the research objects. The entropy method is employed to measure the digital economy and economic resilience development indices from 2011 to 2023. The coupling coordination degree model is then used to systematically investigate the spatiotemporal evolution characteristics and linkage mechanisms of the coupling coordination relationship between the two. The results show that: 1) The development of the digital economy in the four provinces and cities exhibits a three-stage pattern of “initial accumulation – rapid growth – steady callback,” with a regional gradient distribution characterized by “Sichuan leading, Chongqing following closely, and Yunnan-Guizhou catching up.” 2) The economic resilience index shows a continuous linear upward trend, with regional gaps gradually narrowing, displaying significant catch-up and convergence features. 3) There is a significant positive synergistic effect between the digital economy and economic resilience. The overall coupling coordination degree shows a continuous upward trend, evolving from a state of imbalance in 2011 to a high-quality coordination level by 2023. The regional pattern is characterized by “Sichuan highest, Chongqing second, and Yunnan-Guizhou relatively close,” with differences in coordination stability across regions. This study enriches the theoretical framework of digital economy empowerment for regional economic resilience and provides empirical evidence and policy references for the four southwestern provinces and cities to optimize digital economy layout, enhance economic resilience, and promote high-quality coordinated development.
digital economy, economic resilience, entropy method, coupling coordination degree, high-quality development
Copyright (c) 2026 Hongxuan Zhu (Author) https://creativecommons.org/licenses/by/4.0
Study on the Impact of ESG Performance on Corporate Profitability
This study utilizes pre-processed data of all A-share listed companies from 2014 to 2024 as the sample. By constructing regression models and mediation effect models, it investigates the transmission mechanism among ESG performance, corporate risk level, and profitability. The findings indicate that better ESG performance is associated with higher corporate profitability. Moreover, superior ESG performance is linked to lower corporate risk levels, which in turn enhances profitability. The contributions of this research lie in its innovative perspective and practical implications: it addresses existing research gaps by exploring the “pathway” between ESG performance and profitability from the perspective of corporate risk level, thereby enriching the literature on the economic consequences of ESG performance. The conclusions also provide valuable references and insights for economic practices by corporations, governments, regulatory authorities, and other stakeholders.
ESG performance, corporate profitability, corporate risk level, empirical study
Copyright (c) 2026 Ming Yang (Author) https://creativecommons.org/licenses/by/4.0
Research on Reputation Spillover and Synergistic Effects of High-ESG-Performance Enterprises: A Case Study of Pang Dong Lai’s Assistance to Yonghui Supermarket
The Environmental, Social, and Governance (ESG) concept has become a core benchmark for assessing corporate sustainable development capabilities. However, existing studies predominantly focus on the economic consequences of a firm’s own ESG performance, with insufficient attention to its external spillover effects. Taking the highly publicized “Pang Dong Lai assisting Yonghui Supermarket” event as the research object, this paper employs a single-case study method and integrates reputation theory, signaling theory, and the resource-based view to deeply analyze the reputation spillover mechanisms and synergistic effects of high-ESG-performance enterprises. The findings reveal that Pang Dong Lai’s reputation spillover follows a “ripple pattern,” diffusing progressively through four hierarchical levels: embedded empowerment, precise empowerment, radiating empowerment, and linkage empowerment. However, the efficacy of this spillover is significantly constrained by the absorptive capacity of the assisted party, industry competition structure, and sustainability of the assistance model. Information resource effects and competitive learning effects constitute the core driving mechanisms of reputation spillover, yet their effectiveness depends on the interaction quality and institutional distance between the assisting and assisted parties. The study further shows that Pang Dong Lai’s reputation capital provided Yonghui Supermarket with a limited “insurance mechanism,” but this buffering effect exhibits clear temporality and conditionality. Although sales at some renovated stores achieved remarkable 5- to 14-fold growth and employee compensation increased by more than 35%, Yonghui’s overall turnaround remains challenging, and capital market reactions remain mixed. This research not only expands the research context of ESG spillover effects but also reveals the enabling conditions and boundaries of reputation spillover, offering valuable dialectical insights for distressed enterprises seeking strategic transformation through leveraging industry benchmarks.
ESG performance, reputation spillover, synergistic effect, ripple pattern, demonstration effect, boundary conditions
Copyright (c) 2026 Ziming Wang (Author) https://creativecommons.org/licenses/by/4.0
ESG Performance and Corporate Financial Resilience: The Mediating Roles of Risk Mitigation and Financing Constraints
This study examines the impact of ESG performance on corporate financial resilience and the underlying mechanisms. Using a sample of Chinese A-share listed manufacturing firms from 2015 to 2022, we construct a multidimensional financial resilience index based on the “resist-recover-reorganize” paradigm and employ fixed-effects models and mediation analysis. The results show that ESG performance significantly enhances corporate financial resilience, with this effect operating through two parallel mediating channels: risk mitigation and financing constraints alleviation. Heterogeneity analyses reveal that the positive effect is more pronounced for firms with better financial health, firms in non-high-tech industries, heavily polluting firms, firms in the Eastern region, and larger firms. These findings provide theoretical contributions to the ESG and resilience literature and offer practical implications for managers, investors, and policymakers seeking to build more resilient organizations in an uncertain environment.
ESG performance, corporate financial resilience, risk mitigation, financing constraints, heterogeneity analysis
Copyright (c) 2026 Yuqi Deng (Author) https://creativecommons.org/licenses/by/4.0
Fund Network Centrality and Stock Price Informativeness: A Mechanism Study Based on Information Transmission and Governance Effects
Using Chinese A-share listed companies from 2014 to 2024 as the sample, this paper constructs a mutual shareholding network of public mutual funds based on complex network theory. It employs social network analysis to measure fund network centrality and empirically examines its impact on stock price informativeness as well as the underlying mechanisms. The results show that fund network centrality significantly reduces stock price informativeness: higher network centrality is associated with stronger stock price synchronicity and lower efficiency in incorporating firm-specific information into prices. This conclusion remains robust after a series of robustness checks, including lagging the explanatory variable by one period, propensity score matching, and excluding special years. Heterogeneity analysis reveals that the negative effect is more pronounced in small-scale firms and in regions with lower levels of marketization, indicating that weaker information environments amplify homogeneous trading behavior among networked institutions. Mechanism analysis explores two potential channels of influence: first, the “information crowding-out” channel, whereby fund network centrality may weaken analysts’ incentives for information discovery and firm monitoring; second, the “agency cost” channel, whereby network centrality may increase the likelihood of collusion between management and institutional investors, thereby raising agency costs and suppressing the release of firm-specific information. This study extends the research framework on capital market pricing efficiency from a micro-network perspective and provides policy implications for regulators to identify and mitigate market risks arising from coordinated institutional behavior.
fund network centrality, stock price informativeness, stock price synchronicity, social network analysis, information transmission
Copyright (c) 2026 Yuxi Li (Author) https://creativecommons.org/licenses/by/4.0
Differentiated Breakthrough: An Effectiveness Study of NetEase Cloud Music’s “Music + Social + Original Content” Ecosystem Positioning — Based on User Retention and Paid Conversion
NetEase Cloud Music launched its app in 2013 and quickly emerged as a dark horse competitor within just a few years. Today, it has grown into one of China's leading music streaming platforms. In China's fiercely competitive music streaming market, NetEase Cloud Music has carved out a niche through its differentiated strategy centered on “Music + Social + Original Content.” This single-case study examines the synergistic mechanism between ecological niche competition and differentiated development, verifying its impact on user engagement, emotional stickiness, retention rates, and payment rates. Findings reveal that the emotional connections fostered by “playlist UGC + music comment socialization” and the original content ecosystem supported by the “Stone Project” collectively offset the platform's copyright disadvantage. This synergy effectively enhances metrics such as the daily active users to monthly active users ratio (DAU/MAU) and paid subscription rates. This research fills a gap in studying macro-level ecological niche competition among music streaming platforms, providing practical insights for industry differentiation strategies.
music streaming industry, NetEase cloud music, Niche competition, differentiated development
Copyright (c) 2026 Jinshuo Zhang (Author) https://creativecommons.org/licenses/by/4.0
Vol. 21 (2026): Proceedings of 2026 International Conference on Green Chemistry and Biomedical Engineering (IC-GCBE2026)
This prestigious gathering brings together leading scientists, engineers, academicians, and industry professionals from around the world to explore the intersection of sustainable chemistry and biomedical advances. At IC-GCBE 2026, we aim to foster meaningful dialogue, inspire groundbreaking research, and accelerate the development of eco-friendly technologies that address pressing global challenges in healthcare and environmental sustainability.
Copyright (c) 2026 IC-GCBE Committee (Author) https://creativecommons.org/licenses/by/4.0
Vol. 20 (2026): Proceedings of 2026 International Conference on Materials Science, Energy Engineering and Automation Control (IC-MSEAC 2026)
IC-MSEAC 2026 will feature keynote speeches delivered by distinguished experts, parallel technical sessions, poster presentations, and academic networking events. The thematic scope covers advanced functional materials, renewable and low-carbon energy technologies, power and energy systems optimization, robotics and automation engineering, control theory, smart manufacturing, and emerging digital-industrial integration. By encouraging both theoretical exploration and practical application, the conference aims to bridge laboratory research with industrial implementation.
IC-MSEAC Conference Organizing Committee
Copyright (c) 2026 IC-MSEAC Conference Organizing Committee (Author) https://creativecommons.org/licenses/by/4.0
Vol. 19 (2026): Proceedings of 2026 International Conference on Financial Technology, Mathematical Statistics, and Business Analytics (ICFMB 2026)
Under the guiding vision of “Advancing Quantitative Innovation for Intelligent Financial and Business Systems,” ICFMB 2026 reflects a strong commitment to strengthening dialogue across disciplinary boundaries. As financial markets become increasingly digitized, data-intensive, and algorithm-driven, the integration of financial technology, mathematical statistics, and business analytics has become indispensable. Robust statistical methodology, computational modeling, and applied business intelligence must operate in concert to address uncertainty, manage risk, and navigate strategic complexity in contemporary economic systems.
Copyright (c) 2026 ICFMB Conference Organizing Committee (Author) https://creativecommons.org/licenses/by/4.0
Vol. 18 (2026): Proceedings of 2026 International Conference on Smart Healthcare and Biomedical Engineering (ICSHBE 2026)
ICSHBE 2026 will provide an international platform for presenting cutting-edge research in areas such as artificial intelligence in medicine, digital health technologies, wearable and implantable devices, biomedical signal processing, medical imaging, health data analytics, telemedicine, and intelligent healthcare infrastructure. As healthcare systems worldwide undergo rapid digital transformation, the integration of engineering solutions with clinical practice has become increasingly vital. This conference seeks to foster dialogue between academia and industry, encouraging collaboration that bridges theoretical research and real-world application.
Copyright (c) 2026 ICSHBE Conference Organizing Committee (Author) https://creativecommons.org/licenses/by/4.0
Vol. 13 (2026): Proceedings of the 3rd International Conference on Business, Management and Sustainability (ICBMS 2026)
With a diverse array of keynote speeches, panel discussions, and networking opportunities, this event is designed to foster innovation and collaboration. Our agenda includes topics that are relevant to the challenges and opportunities facing modern businesses, with a special focus on sustainable practices that promote long-term success and environmental stewardship. Whether you are an academic looking to present your research, a business professional seeking to expand your knowledge, or a policy maker interested in sustainable development, the International Conference on Business, Management, and Sustainability promises to be a valuable and enriching experience.
Copyright (c) 2026 ICBMS Conference Organizing Committee (Author) https://creativecommons.org/licenses/by/4.0
Vol. 14 (2026): Proceedings of the 2nd International Conference on Artificial Intelligence, Modern Engineering and Environmental Sustainability (IC-AIMEES 2026)
IC-AIMEES 2026 seeks to bridge theoretical advancements with real-world applications, fostering collaborations that accelerate the transition toward resilient, low-carbon economies. By integrating cutting-edge AI, transformative engineering practices, and environmental ethics, the conference aims to: Propel breakthroughs in sustainable technology deployment. Establish actionable frameworks for the UN Sustainable Development Goals (SDGs). Cultivate partnerships across academia, industry, and governance.
IC-AIMEES Conference Organizing Committee
Copyright (c) 2026 IC-AIMEES Conference Organizing Committee (Author) https://creativecommons.org/licenses/by/4.0
Application Prospects and Challenges of Micro-Nano Memristors
This paper reviews the basic structure, material systems, operating principles, and application progress of memristors. First, the typical metal-insulator-metal (MIM) structure of memristors is introduced, along with the characteristics of various insulator materials such as metal oxides, two-dimensional materials, and phase-change materials. Subsequently, research progress in simulating biological synapses, achieving physically non-clonable functions, and enhancing data processing efficiency is outlined across various domains including neuromorphic computing, hardware security, data preprocessing, and multi-level storage. Despite advantages in storage density and computational approach, resistive memory devices currently face challenges such as unstable resistance states and inherent device-to-device variations, limiting their readiness for large-scale practical deployment. Recent efforts in material doping and interface engineering have yielded improvements in device uniformity and stability. However, further optimization of material systems and device structures remains essential to enhance reliability and controllability for future applications.
nanoscale memristors, neuromorphic computing, hardware security, machine learning
Copyright (c) 2026 Xiangyi Li (Author) https://creativecommons.org/licenses/by/4.0
A Review of Industrial Fault Diagnosis Technologies Based on Machine Learning
The stable operation of industrial equipment is a fundamental guarantee for intelligent manufacturing, and fault diagnosis technologies have evolved from experience-driven and model-driven approaches to data-driven paradigms based on machine learning. In response to challenges such as nonlinearity, strong coupling, and the scarcity of fault samples in industrial systems, machine learning has become a core approach for fault diagnosis due to its advantages in feature learning and pattern recognition. This paper systematically reviews the research progress of machine learning in industrial fault diagnosis. From the perspective of technical frameworks, it elaborates on the application principles and engineering performance of traditional machine learning, deep learning, and improved algorithms. Furthermore, it analyzes algorithm adaptability in typical industrial scenarios, identifies key challenges such as small-sample diagnosis and model interpretability, and finally discusses future development trends from the perspectives of algorithm integration and data augmentation. This study aims to provide references for technological optimization and engineering applications in this field.
machine learning, industrial fault diagnosis, feature extraction, deep learning, small-sample learning, intelligent diagnosis
Copyright (c) 2026 Siying Chen (Author) https://creativecommons.org/licenses/by/4.0
As the 2028 Los Angeles Olympics approach, accurately predicting the distribution of medals has become a critical issue in the fields of sports science and decision-making. This study addresses the challenges traditional models face in capturing the cyclical nature of Olympic data and the influence of political factors by proposing a multi-model integration forecasting framework. The aim is to improve the accuracy of predictions for the 2028 medal tally by quantifying historical trends, external political variables, and coaching effects, while identifying countries with the potential to achieve a “breakthrough” in their medal count. The study employs Seasonal and Trend Decomposition using Loess (STD-Loess) and Seasonal Differencing to construct an ARIMAX-based Seasonal Empirical Forecasting Model that incorporates participation frequency for predicting the total number of medals won by each country. It integrates Markov Chains and Bayesian updating to construct the First Medal Prediction Model for forecasting the first medal won. A Tobit model and the PageRank algorithm are used to evaluate the contribution of specific sports disciplines (SDE), and segmented regression is employed to quantify the “great coach” effect. The Seasonal Empirical Forecasting Model outperformed XGBoost in terms of prediction accuracy. Forecasts indicate that the United States (98 medals, range [37, 145]) and China (86 medals, range [43, 130]) will lead the medal count, while the Czech Republic is projected to achieve a 120% increase in medals. Nepal is deemed the most likely country to win its first medal in 2028, with odds of 1.23. Furthermore, the study found that men’s basketball accounts for as much as 57% of the U.S. team’s total medal count and confirmed the critical role of coaching intervention in achieving a breakthrough in gold medals. Through an innovative model integration approach, this study provides National Olympic Committees with a scientific basis for resource allocation and recommendations for coach recruitment, while profoundly revealing the centralization of modern Olympic medal distribution and the trend toward international competition.
medal predictions, ARIMAX, Markov Chain, Tobit, model integration, analysis of inﬂuencing factors
Copyright (c) 2026 Anlan Zheng, Yiran Tang (Author) https://creativecommons.org/licenses/by/4.0
The Closed-Loop Construction of Soft Robot Perception, Decision-Making, and Control Under the Cyber-Physical Systems (CPS) Framework
This paper aims to explore the “Sense-Plan-Act” closed-loop construction of soft robot within the Cyber-Physical System (CPS) framework. The soft robot is deconstructed into the perception layer, decision layer, and control layer, and the organic combination of these three layers to form an effective closed-loop system is elaborated. Initially, within the perception layer, key challenges and advanced technologies for soft robot state acquisition are discussed, including the integration of multi-modal sensors and data fusion methods. Furthermore, the application of soft sensors to address the perception challenges arising from complex deformations is examined. Subsequently, within the decision layer, the architectures and algorithms for information processing, state estimation, and intelligent decision-making are analyzed, with a particular focus on leveraging artificial intelligence (AI) and machine learning (ML) technologies to extract meaningful information from noisy data and enable robust decision-making. Finally, in the control layer, the actuation system and the challenges and progress of its practical implementation will be explored. This includes adaptive control strategy and human-robot collaboration methods, which are crucial for ensuring the precise and safe operation of the soft robot in complex environments. Through the establishment of this framework, this review aims to provide comprehensive theoretical support and guidance for the research and application of soft robot within a CPS context.
cyber-physical system (CPS), soft robot, machine learning, human-robot collaboration
Copyright (c) 2026 Yang Yue (Author) https://creativecommons.org/licenses/by/4.0
Vol. 17 (2026): Proceedings of the International Conference on Research in Accounting, Finance and Management (ICRAFM 2026)
The conference provides a platform for researchers and scholars to present and discuss work in accounting, finance, and management. ICRAFM 2026 invites submissions including empirical studies, theoretical research, and practice-oriented papers that contribute to these fields. Participants will engage in paper sessions, panel discussions, and networking opportunities with academics from various institutions.
Copyright (c) 2026 ICRAFM Conference Organizing Committee (Author) https://creativecommons.org/licenses/by/4.0
Vol. 16 (2026): Proceedings of the 2026 International Conference on AI and Technological Innovation Applications (AITIA 2026)
We cordially invite researchers, scholars, and practitioners from around the world to submit their latest research findings to the 2026 International Conference on AI and Technological Innovation Applications (AITIA 2026). This prestigious event provides an exceptional platform to showcase your work, engage with leading experts, and contribute to the advancement of artificial intelligence and technological innovation.
Copyright (c) 2026 AITIA Conference Organizing Committee (Author) https://creativecommons.org/licenses/by/4.0
Vol. 15 (2026): Proceedings of the 2026 International Conference on Mathematics, Physics and Earth Sciences ( IC-MPES 2026)
At IC-MPES 2026, we believe that the most significant breakthroughs in science emerge from the convergence of ideas across traditional disciplinary boundaries. Mathematics provides the elegant language of the universe; Physics uncovers the fundamental laws governing matter and energy; Earth Sciences reveal the dynamic processes that shape our planet. Together, these fields form the cornerstone of human understanding of the natural world, and our conference serves as a vital intersection where ideas collide, inspire, and evolve.
Copyright (c) 2026 IC-MPES Conference Organizing Committee (Author) https://creativecommons.org/licenses/by/4.0
Design of Lightweight Vision System for Agricultural Drones: Real-time Identification of Crop Diseases under Complex Lighting Conditions
Global food security is facing multiple pressures such as shrinking arable land and extreme weather, driving agriculture towards high precision and intelligence. Unmanned aerial vehicles (UAVs), as an aerial monitoring platform, provide important technical support for crop disease monitoring with their mobility and imaging capabilities. However, the complex lighting conditions in the field have led to a decline in image quality, and the limited computing power of the embedded platform has severely restricted the accuracy and real-time performance of disease identification. For this reason, this paper systematically reviews the latest progress in lightweight visual model design and light enhancement methods. In terms of model lightweighting, channel pruning, quantization techniques and knowledge distillation strike an effective balance between accuracy and efficiency; In terms of illumination enhancement, from traditional Retinex algorithms to physically guided generative adversarial networks, there is a significant improvement in image quality and model robustness. Future research could focus on areas such as polarization imaging and federated learning to further enhance the generalization ability and reliability of the system in complex environments.
agricultural drones, lightweight vision systems, crop disease identification, complex lighting robustness, edge computing deployment
Copyright (c) 2026 Yinlai Zhang (Author) https://creativecommons.org/licenses/by/4.0
Parkinson’s Disease: Calcium Dysregulation as a Multilevel Mechanism of Selective Neuronal Vulnerability, with Implications for Therapeutic Strategy
Parkinson’s disease (PD) is a prevalent neurodegenerative disorder characterised by the selective degeneration of dopaminergic neurons in the substantia nigra pars compacta. Despite extensive investigation into genetic and environmental risk factors, the mechanisms underlying this selective neuronal vulnerability remain incompletely understood. Increasing evidence suggests that calcium dysregulation represents a unifying pathogenic mechanism linking molecular, cellular, and systems-level processes in PD. This review synthesises evidence demonstrating how the intrinsic reliance of substantia nigra dopaminergic neurons on calcium-dependent autonomous pacemaking imposes a sustained metabolic burden that predisposes these neurons to degeneration. It examines how genetically diverse PD-associated mutations, including those affecting PINK1/Parkin, LRRK2, and GBA, converge on disrupted calcium homeostasis through distinct cellular pathways involving mitochondrial buffering, lysosomal calcium signalling, and organelle quality control. At the cellular level, chronic calcium dysregulation drives oxidative stress, α-synuclein aggregation, and impaired autophagy, forming a self-reinforcing cycle of neuronal dysfunction. These processes extend beyond individual neurons, promoting neuroinflammation and basal ganglia circuit reorganisation that ultimately underlie motor symptom expression. The review further discusses the therapeutic implications of this multilevel framework, highlighting why broad calcium channel blockade has shown limited clinical efficacy. It argues that future disease-modifying strategies will require greater molecular and temporal precision, as well as integration with systems-level neuromodulation approaches that reduce activity-dependent calcium and metabolic burden. Overall, calcium dysregulation emerges as a central integrator of vulnerability across biological scales in Parkinson’s disease, offering a coherent framework for understanding pathogenesis and guiding therapeutic innovation.
Parkinson’s disease, calcium dysregulation, selective neuronal vulnerability, dopaminergic neurons, α-synuclein pathology
Copyright (c) 2026 Weijie Lai (Author) https://creativecommons.org/licenses/by/4.0
A Discussion on the Similarities Between Mechanical Metamaterials and Soft Matter and the Possibilities for Their Combined Development
Mechanical metamaterials and soft matter, as frontier branches of materials science, extend the performance boundaries of conventional materials through artificial structural design and intrinsic responsive behavior, respectively. This paper briefly introduces these two fields and examines their similarities. It further discusses the possibility of their innovative integrated development as an approach to addressing certain limitations in each field, such as employing the stimulus responsiveness of soft matter to overcome the functional static nature of mechanical metamaterials, and utilizing the structural design paradigm of mechanical metamaterials to enhance the response speed and fatigue resistance of soft matter.
mechanical metamaterials, soft matter, similarity, integrated development
Copyright (c) 2026 Jiarui Zheng (Author) https://creativecommons.org/licenses/by/4.0
Applications of Machine Learning in Additive Manufacturing
The research aims to solve common challenges in additive manufacturing processes, including strong multi-parameter coupling, difficulty in suppressing defects, and unstable performance, while overcoming the limitations of traditional empirical trial-and-error and physics-based modeling approaches and advancing additive manufacturing toward intelligent, automated, and closed-loop upgrades. The methodology centers on machine learning’s data-driven paradigm, integrating various algorithms such as neural networks, random forests, and support vector machines. From the full life cycle of additive manufacturing, the study systematically explores technical pathways and application modes in process monitoring, defect control, parameter optimization, performance prediction, structural design, and material development. Results show that machine learning can effectively construct the “process–structure–performance–defect” mapping relationship in additive manufacturing. In online monitoring, defect identification accuracy exceeds 95%; process development cycles can be shortened from weeks to days, and new material R&D cycles from months to weeks. High-precision prediction of component mechanical properties is also achieved, with some models attaining R² values above 0.9. The conclusion is that machine learning provides a full-process data-driven optimization solution for additive manufacturing and effectively addresses many pain points of traditional methods. Current applications in this field still face challenges such as insufficient data quality and quantity and poor model interpretability. Future efforts should focus on physics-informed machine learning, digital-twin closed-loop control, and other directions, while advancing standardization, to promote the engineering implementation and scaled production of intelligent additive manufacturing and provide support for the development of high-end equipment, biomedicine, and other fields.
machine learning, additive manufacturing, process optimization, defect detection
Copyright (c) 2026 Zicheng Qu (Author) https://creativecommons.org/licenses/by/4.0
Limitations of AI-Generated Content and Strategies for Enhancement
With the rapid advancement of artificial intelligence (AI) technologies, tools such as Doubao and DeepSeek have become indispensable elements in everyday life. Many users turn to these AI platforms for secondary editing of images and videos or to generate visuals that align with specific textual descriptions. However, the outcomes frequently fall short of expectations: the generated content often bears little resemblance to the user’s prompts and is riddled with unacceptable flaws. To mitigate these issues and enable AI tools to produce satisfying results that meet given specifications, this paper explores the underlying principles of AI content generation. It proposes methods to enhance the accuracy of outputs and reduce inherent defects, concluding with a discussion of the study’s limitations.
AI-generated content (AIGC), limitations, enhancement strategies
Copyright (c) 2026 Weihang Gao (Author) https://creativecommons.org/licenses/by/4.0
Demand-Oriented Renovation Strategies for Public Spaces in University Dormitory Squares: A Case Study of the LZH Campus at North China University of Water Resources and Electric Power
As a key node in the campus public space system, the small squares in dormitory areas carry important functions for students’ informal learning, social interaction, and leisure relaxation, making them one of the significant public activity spaces on campus. As high-frequency daily public spaces frequently used by students, the current quality of university dormitory area small squares stands in sharp contradiction with students’ growing diversified and individualized needs. Based on demand theory, environmental behavior studies, participatory design, and other theoretical foundations, this study employs methods such as literature analysis, case studies, and field surveys to systematically investigate the existing problems in current dormitory small squares, including underutilized facilities, single-function layouts, insufficient vitality, and lack of humanistic care. The research shows that in the renovation process, priority must be given to responding to the common “basic needs” related to safety, shelter, and wayfinding, while strategically integrating and elevating the differentiated “expectation needs” concerning leisure, socialization, and aesthetic enhancement. The core lies in adopting a student demand-oriented approach and implementing four major strategies—functional compounding, flexible boundaries, natural integration, and smart empowerment—to construct a “dormitory living room” that promotes interaction and stimulates vitality. This study provides a theoretical framework and practical pathway for the “micro-renewal” of existing public activity spaces in university dormitory areas. It holds significant reference value for improving the quality of campus living environments and promoting student community integration.
demand-oriented, environmental behavior research, micro-renewal, public space renovation
Copyright (c) 2026 Jiaxuan Yu (Author) https://creativecommons.org/licenses/by/4.0