Simon Owen Academic Proceedings Series is an international, refereed, double-blind peer-reviewed, open-access journal that compiles the proceedings of academic conferences, workshops, and symposia across various disciplines. The series highlights groundbreaking research, emerging trends, and innovations presented by scholars, researchers, and practitioners in their respective fields. With a focus on high-quality contributions, the series serves as a valuable resource for the academic community, facilitating the dissemination of knowledge, fostering collaboration, and offering insights into the latest developments in science, technology, and academia.
Optimization of Power Systems for Low-Carbon Manufacturing in New Energy Vehicles
The transition toward carbon neutrality has intensified the need for low-carbon manufacturing in the new energy vehicle (NEV) industry. While existing research has primarily focused on the use phase of vehicles, limited attention has been given to the carbon emissions and energy consumption arising during the development and manufacturing of power systems. To address this gap, this study establishes an integrated Energy-Carbon-Cost (ECC) framework that combines lifecycle assessment (LCA), empirical industrial data, and multi-objective optimization modeling. The framework quantifies how design parameters, such as material substitution, motor efficiency, and cooling configurations, affect lifecycle carbon intensity, cost, and energy efficiency. Results based on case studies from SAIC, BYD, and Volkswagen demonstrate that optimized configurations can reduce total lifecycle carbon emissions by approximately 24% while maintaining economic feasibility. The proposed model transforms traditional LCA from a static evaluation tool into a dynamic, prescriptive decision system, enabling both theoretical innovation and practical applicability. This research provides manufacturers and policymakers with an actionable pathway for achieving synergistic carbon reduction and cost optimization, contributing to the sustainable advancement of NEV manufacturing.
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Electronic Coupling Effects of 2D Material Heterostructures and Their Applications in Flexible Optoelectronic Devices
In the post-Moore era, flexible optoelectronic devices are rapidly advancing towards wearable and portable applications, placing higher demands on material optoelectronic performance and mechanical flexibility. Single 2D materials have inherent limitations, graphene lacks a bandgap, limiting photoelectric conversion efficiency; transition metal dichalcogenides (TMDs) like MoS₂ have low carrier mobility; and black phosphorus is prone to oxidation, affecting long-term stability. These challenges are difficult to address through single material modification. 2D material heterostructures, formed through interlayer composite structures, leverage electronic coupling effects to enhance performance synergistically, providing an effective solution to overcome these limitations. This paper reviews common construction methods for typical 2D heterostructures, such as graphene-based, TMDs-based, and black phosphorus-based structures, including van der Waals stacking, covalent bonding, and solution assembly. It focuses on the mechanisms of interlayer charge transfer, exciton transfer, and orbital hybridization, and discusses how layer number control, strain application, and interface modification influence these coupling effects. Additionally, this work surveys the application of these heterostructures in flexible photodetectors, flexible light-emitting diodes (LEDs), and flexible solar cells, comparing their photoelectric response speed, energy conversion efficiency, and bending stability. Finally, challenges such as interlayer delamination, environmental instability, and difficulties in large-scale fabrication are discussed, with future directions in interface engineering and multifunctional heterostructure integration proposed for enhancing practical applications.
Research on Innovative Strategies of Physics Experiment Teaching in Senior High School Based on the Cultivation of Physics Core Literacy
This paper focuses on high school physics experiment teaching based on the cultivation of physics core literacy. Firstly, the connotation of physics core literacy, the characteristics of physics experiment teaching in senior high school and their relationship are expounded, and the problems existing in teaching objectives, contents, methods, evaluation and teachers' literacy are pointed out. Then put forward innovative strategies, including optimizing the design of teaching objectives and running the core literacy elements through the whole process; Innovating teaching content, combining reality with cutting edge; Improve teaching methods and means and make use of modern technology; Improve the evaluation system and consider the process and results; Strengthen the improvement of teachers' professional quality. Through the comprehensive application of these strategies, it is expected to improve the teaching quality and cultivate students' core literacy in physics.
AliExpress's Internationalization: Strategies and Practices on Cross-Border E-Commerce Platforms
This study conducts an in-depth examination of AliExpress's globalization strategies on cross-border e-commerce platforms, highlighting how the integration of "multi-tier ecological empowerment" and "glocalization" strategies fosters systemic innovation across five critical dimensions: market selection, platform operations, product assortment and pricing, logistics and payment, and marketing promotion. The findings indicate that AliExpress adopts a "mature-emerging" dual-track model for global market expansion: leveraging overseas warehouses and localized services to enhance timeliness in European and American markets, while deploying strategies such as "cross-border express delivery" to penetrate emerging markets including Southeast Asia. In addition, the company has developed a four-dimensional operational matrix, "POP self-operation + full hosting + semi-hosting + overseas hosting", to empower diverse types of sellers. Supported by data scraping and flexible testing mechanisms, the full-hosting model has increased the blockbuster success rate to 67%. By constructing six million square meters of overseas warehouses and implementing blockchain-based settlement technologies, AliExpress has optimized logistics and payment experiences. Moreover, the platform harnesses social media channels such as TikTok to achieve viral marketing at scale. These strategies have positioned AliExpress among the global top three in personalized shopping experience in PYMNTS rankings, while its share of organic traffic has risen to 63%. The paper also underscores the challenges AliExpress faces, including compliance risks, insufficient localization depth, and ESG-related pressures, and proposes targeted optimization measures. Overall, the research not only offers a replicable pathway for the globalization of Chinese manufacturing but also provides important insights for the future evolution of the cross-border e-commerce industry.
Graph Neural Networks for Business Relationship Mining: Applications and Performance Analysis
Business ecosystems, such as supply-chain networks, financial transaction systems, and e-commerce platforms, exhibit complex relational structures that challenge traditional machine-learning models. Although graph neural networks (GNNs) have shown promise in capturing such dependencies, existing studies often focus on single domains, rely on static graphs, or lack systematic comparison across heterogeneous commercial settings. To address these gaps, this study proposes a unified analytical framework that integrates relational embeddedness theory, graph representation learning, and dynamic capability perspectives. Using three representative real-world scenarios, a retail procurement graph, an AML transaction network, and an e-commerce product affinity graph, we evaluate four GNN architectures (GCN, GraphSAGE, GAT, and Temporal-GNN) through link prediction, fraud detection, and recommendation tasks. The results show that attention-based models outperform others in heterogeneous supplier and transaction environments, temporal GNNs better capture evolving fraud patterns, and inductive architectures excel in high-turnover product graphs. These findings deepen theoretical understanding of relational learning in commercial systems and offer practical guidance for deploying GNN-based analytics in procurement risk assessment, financial compliance, and personalized recommendation services.
AI-Driven Optimization of Healthcare Resources: An Intelligent Management Model for Hospital Operational Efficiency in the Post-Pandemic Era
The COVID-19 pandemic has underscored the critical need for flexible and adaptive hospital management systems to handle fluctuating patient volumes and constrained medical resources. Traditional resource allocation methods, relying on fixed schedules and static capacity planning, are increasingly inadequate in addressing the dynamic challenges faced by hospitals. This study addresses this gap by proposing an AI-driven hospital resource optimization model, integrating demand forecasting, dynamic scheduling, and system simulation. The model was evaluated using real-world hospital data, demonstrating significant improvements in operational efficiency, including a 20% reduction in patient waiting times and a 33% increase in bed turnover rate. The AI model also enabled proactive staff scheduling and bed management during peak demand periods, such as flu seasons. This research contributes to the understanding of hospitals as complex adaptive systems and provides a replicable framework for optimizing resource allocation. The findings have significant implications for improving hospital resilience, operational efficiency, and patient care quality, particularly in post-pandemic healthcare settings. Future work will focus on refining the model for multi-hospital applications and addressing the challenges of data integration and ethical concerns in AI decision-making.
Single-Atom Catalysts Based on Two-Dimensional Transition Metal Carbides for Efficient Carbon Dioxide Reduction
The electrochemical reduction of carbon dioxide (CO₂RR) into value-added products is a promising route toward carbon neutrality, yet its efficiency is often limited by the weak activity and instability of conventional catalysts. Single-atom catalysts (SACs) anchored on two-dimensional transition metal carbides (MXenes) offer a tunable platform with high atomic utilization and strong metal-support interactions. However, the fundamental understanding of how coordination geometry and electronic coupling jointly regulate CO₂ activation remains insufficient. This study integrates density functional theory (DFT) calculations with experimental electrochemical characterization to investigate MXene-supported SACs. The Fe₁/Ti₃C₂O₂ system exhibits a pseudo-square-planar coordination environment with strong Fe-O bonding, leading to an optimized d-band alignment and enhanced σ-back-donation to CO₂π* orbitals. The calculated free-energy barrier for COOH formation is only 0.41 eV, resulting in high Faradaic efficiency (91.2%) and a partial current density of 34.8 mA·cm⁻². These findings reveal that ligand field stabilization and interfacial charge transfer are key to achieving efficient CO₂ reduction. The proposed coordination-electronic coupling framework provides actionable guidelines for the rational design of high-performance catalytic materials in sustainable energy conversion.
Energy-Adaptive Micro-Mechanical Systems Driven by Intelligent Materials: Experimental and Simulation Study Based on the Piezoelectric Effect
The miniaturization of mechanical and electronic systems has driven demand for self-powered micro-electro-mechanical systems (MEMS) in robotics, biomedical implants, and distributed IoT networks. Piezoelectric materials offer direct mechanical-to-electrical energy conversion, making them promising candidates for energy-autonomous microsystems. Existing studies largely optimize either material performance or system-level design independently, neglecting the integration of microstructural electromechanical behavior with adaptive energy regulation. Moreover, nonlinear piezoelectric responses under micro-scale cyclic loading remain insufficiently quantified, leading to 15-30% discrepancies between simulations and experiments. This work develops a hybrid PZT-PVDF composite thin film integrated into a cantilever MEMS with a closed-loop adaptive feedback controller. A coupled multi-physics finite element model links stress-strain fields to voltage output, while real-time stiffness adjustment maximizes energy harvesting efficiency under variable vibration conditions. Experimental validation spans 150 samples with cyclic loading across 50-500 Hz. The proposed adaptive MEMS achieves a peak voltage of 5.8 ± 0.2 V, output power density of 2.45 ± 0.05 mW·cm⁻³, and conversion efficiency of 77.1 ± 1.1%, representing a 28% improvement over non-adaptive hybrid systems. Voltage degradation after 10⁵ cycles is limited to 3.2 ± 0.4%, and the system converges to steady-state power within 12 ± 2 control iterations. By integrating material-level physics, multi-physics modeling, and adaptive control, this framework enhances energy conversion reliability and provides a reproducible approach for designing energy-autonomous MEMS suitable for micro-robots, implantable sensors, and distributed IoT devices.
Evaluating the Effectiveness of Legal Regulation of AI-Generated Content and Optimizing Regulatory Pathways
The rapid expansion of generative artificial intelligence (AI) has transformed fields such as media, education, and politics, while simultaneously raising urgent legal and ethical challenges. Issues including disinformation, copyright disputes, and threats to democratic processes have intensified debates on how best to regulate AI-generated content. Yet existing research often focuses on single jurisdictions, lacks systematic evaluation criteria, and rarely explores pathways for globally adaptive governance, leaving both scholars and policymakers without a coherent framework for assessing effectiveness. This study addresses these gaps by developing a multidimensional evaluative framework grounded in technology-law co-evolution, risk-benefit balancing, and human rights-based governance. Employing comparative legal analysis, doctrinal review, and case studies from the European Union, the United States, and China, the research assesses regulatory effectiveness across three dimensions: enforceability, adaptability, and rights protection. Findings indicate that the EU ensures comprehensive safeguards but struggles with consistent enforcement, the U.S. emphasizes expressive freedom but remains fragmented, and China achieves strong compliance at the cost of transparency and rights. The paper contributes theoretically by integrating diverse perspectives into a coherent model and practically by proposing a hybrid regulatory pathway that combines hard law, soft law, and oversight mechanisms. This approach provides a roadmap for more balanced and adaptive governance of AI-generated content.
Applications and Developments in Semiconductor Memory Technology
With the rapid evolution of information technology, semiconductor memory has become a cornerstone of modern computing, supporting big data, artificial intelligence, and the Internet of Things. Traditional magnetic storage technologies are increasingly unable to meet demands for speed, capacity, and energy efficiency, highlighting the urgency of advancing semiconductor memory research. Despite progress in DRAM, SRAM, and flash memory, existing studies reveal gaps in addressing production sustainability, high costs, and environmental challenges. This paper systematically reviews the development and industrial landscape of semiconductor memory, analyzes its manufacturing processes and associated pollutants, and evaluates emerging sustainable solutions such as advanced abatement systems and wastewater treatment strategies. It further explores cutting-edge alternatives, particularly Resistive Random Access Memory (ReRAM), which integrates high-speed and non-volatile features but still suffers from device variability, circuit complexity, and limited market readiness. Results show that innovative process optimization and architecture-aware training can partially mitigate these constraints, while sustainable practices significantly reduce environmental impacts. The study underscores that advancing semiconductor memory requires not only technological breakthroughs but also robust sustainability and intellectual property strategies, offering critical implications for global competitiveness, industrial upgrading, and the transition toward low-carbon intelligent infrastructures.
Optimization Analysis of Stability and Deformation Control Methods for Deep Excavation Support Structures Based on Field Measurements
Deep excavation support systems in urban environments present demanding engineering challenges due to their inherent stability risks and potential for inducing detrimental deformations in adjacent infrastructure. Conventional design methodologies often inadequately address the complex soil-structure interaction dynamics and temporal construction effects, leading to either excessive conservatism or unforeseen performance issues. This study develops an integrated optimization framework that synergistically combines high-frequency field instrumentation data with advanced computational modeling to enhance the stability and deformation control of deep excavation support structures. The proposed methodology employs a physics-informed Bayesian calibration approach to continuously update finite element models using real-time measurements from inclinometers, strain gauges, and piezometers. A multi-objective optimization algorithm subsequently identifies optimal support configurations that simultaneously maximize stability margins, minimize deformation, and reduce material costs. Validation through a major metropolitan excavation case study demonstrates that this field measurement-driven approach achieves significant improvements in deformation control while maintaining structural integrity. The framework's ability to adaptively refine support designs during construction phases offers substantial advancements over static design paradigms. By transforming conventional excavation support into a responsive, data-informed process, this research provides a foundation for intelligent infrastructure development in spatially constrained urban environments.
Dynamic Scheduling Strategy for Complex Industrial Processes Based on Multi-Agent Collaboration
The increasing complexity of modern industrial processes, characterized by frequent disturbances such as equipment failures and urgent order changes, demands more adaptive scheduling solutions. Traditional centralized scheduling methods often fail to address real time dynamics, while existing multi agent systems face challenges in balancing local autonomy with global optimization. This study proposes a novel dynamic scheduling strategy integrating multi agent collaboration with a credit based coordination mechanism to enhance responsiveness and efficiency in complex industrial environments. The research develops a three layer agent architecture comprising resource, task, and coordinator agents, linked through an event driven communication protocol. A hybrid negotiation framework enables both rapid response to emergencies and deliberative optimization for long term scheduling. The core innovation lies in a dynamic credit allocation model that evaluates agents' historical performance and collaborative contributions to guide task assignment. These findings advance distributed industrial control theory by formalizing the relationship between agent incentives and system wide performance. The proposed approach provides actionable insights for implementing Industry 4.0 adaptive scheduling in discrete manufacturing sectors.
Research and Optimization of a Real-Time Quality Monitoring System for Smart Production Lines Based on IoT Sensors
The rise of Industry 4.0 has accelerated the adoption of IoT-enabled sensing for real-time quality assurance in smart manufacturing. However, most existing systems depend on cloud-centric analytics or supervised learning models that require extensive labeled defect data, leading to latency, poor adaptability, and limited applicability in dynamic production environments. To address this gap, this study proposes an IoT sensor-driven quality monitoring framework based on multi-modal signal acquisition, edge computing, and adaptive thresholding informed by short-term process variability. The system was deployed and evaluated on two industrial production lines, Bosch automotive components and CATL lithium-ion module assembly, using longitudinal tracking of defect rate, first-pass yield, and overall equipment effectiveness. Results indicate a 26-31% reduction in defects, a 26.9% increase in first-pass yield, and a 7% improvement in OEE, alongside a 47.8% decrease in false alarms compared with static control methods. These findings demonstrate that real-time adaptive monitoring can enhance quality performance without dependency on large labeled datasets. The study provides a replicable implementation methodology and insights into sensor contribution, offering practical guidance for scalable deployment and future advancements in intelligent quality control.
Scalable Distributed Systems for Real-Time Big Data Processing in Financial Technology
Real-time stream processing in regulated financial environments requires simultaneous guarantees of low latency, data confidentiality, and auditability, requirements that existing systems struggle to satisfy jointly. Prior approaches either sacrifice performance for security or omit compliance mechanisms entirely, leaving a gap in practical, production-ready solutions. To address this, we propose a co-designed architecture integrating lightweight secure aggregation (LSA), adaptive micro-batching, and LSTM-based predictive autoscaling within Apache Flink. Evaluated on a real-world dataset of anonymized payment transactions, our system achieves a 99th-percentile latency of 178 ± 6 ms at a sustained throughput of 89k ± 1.2k events/sec, thereby meeting a strict 200-ms service-level objective while maintaining 100% compliance completeness. In contrast, a baseline employing homomorphic encryption (CryptoStream) incurs a significantly higher latency of 312 ± 18 ms and consumes roughly four times the CPU resources. Another secure baseline (Flink-SGX), while meeting the latency target (192 ± 9 ms), exhibits operational fragility under load. Ablation studies confirm the necessity of each component for balancing performance, stability, and regulatory adherence. Collectively, the results demonstrate a feasible path toward confidential, auditable, and high-performance stream processing for real-world financial infrastructure.
High-Performance Computing in Deep Learning: Distributed Training Strategies for Transformer Models in Natural Language Processing
Distributed training of large Transformer models is increasingly conducted on heterogeneous high-performance computing (HPC) clusters, where variability in compute capacity and network topology degrades efficiency and stability. Existing systems rely on static partitioning or uniform gradient compression, leading to communication bottlenecks, suboptimal convergence, and poor fault tolerance. To address these limitations, we propose an adaptive distributed training framework that integrates topology-aware model placement, layer-wise adaptive sparsification based on gradient variance, and error feedback with hybrid parallelism. Evaluated on a 1.3-billion-parameter Transformer across 32 GPUs (including RTX 4090 and V100), our method achieves a throughput of 2,268 ± 29 samples/sec (23.1% higher than Megatron-LM) and reduces time to target validation loss (<2.85) to 12.8 ± 0.2 hours (12.9% shorter than Megatron-LM and 25.1% shorter than DeepSpeed ZeRO-2 (p < 0.001)). Communication volume is lowered to 2.03 ± 0.02 GB/step (approximately 58% lower than Megatron-LM), and the robustness score reaches 0.92 ± 0.01. The approach maintains competitive out-of-domain perplexity (PubMed: 14.2; GitHub: 18.7) and recovers from 5% node failures in 30 ± 3 steps. These results demonstrate a practical path toward efficient, stable, and deployable large-model training in shared, heterogeneous infrastructure.
Optimization of Silicon Nanocomposite Anodes for High-Energy-Density Lithium-Ion Batteries
Lithium-ion batteries (LIBs) are vital for energy storage in applications like consumer electronics and electric vehicles. Silicon-based anodes offer high theoretical capacity, but challenges remain, including large volume expansion, low Coulombic efficiency, and short cycle life. Current strategies, such as nanostructuring and carbon hybridization, struggle to balance performance with scalability and durability. This study introduces a silicon-graphene-polyacrylic acid (Si/G/PAA) nanocomposite anode, integrating structural, interfacial, and processing innovations. The key advancement is the dual-phase confinement of Si nanoparticles using reduced graphene oxide (rGO) and crosslinked PAA, along with engineered mesoporosity to accommodate Si expansion and interfacial passivation via GO wrapping. The process is water-based and NMP-free, ensuring industrial scalability. The Si/G/PAA anode achieves an initial Coulombic efficiency of 83 ± 1% and 85 ± 2% capacity retention after 200 cycles at 3.5 mAh cm⁻² areal capacity. Full-cell tests with LiFePO4 cathodes at 45°C show over 80% capacity retention after 300 cycles. This work provides a scalable, high-performance anode solution, addressing key LIB challenges and demonstrating both academic and practical significance for future energy storage technologies.
Performance Enhancement of Piezoelectric Nanomaterials for Intelligent Sensing and Their Electronic Signal Processing Solutions
Piezoelectric nanomaterials, such as ZnO and PZT nanowires, are widely used in flexible electronics, health monitoring, and IoT sensors due to their ability to convert mechanical energy into electrical signals. However, challenges remain in optimizing their piezoelectric performance and improving signal processing for real-world applications. While significant progress has been made in material enhancement and signal processing, there is a lack of comprehensive integration between these two approaches, limiting the performance of piezoelectric sensors in dynamic environments. This study investigates the impact of material enhancements (doping and structural optimization) on the piezoelectric properties of ZnO and PZT nanowires and explores various signal processing techniques (filtering, amplification, noise reduction) to improve sensor performance. The effectiveness of these methods is validated through experimental testing and performance evaluation. The results show that Li-doped ZnO and optimized PZT nanowires significantly improve piezoelectric performance. Signal processing techniques, particularly low-pass filtering and adaptive amplification, increase the signal-to-noise ratio (SNR) by 25%, with overall sensor sensitivity improving by 30%. The findings contribute to the development of more efficient piezoelectric sensors by integrating material enhancement and signal processing, offering significant improvements in sensor performance for applications in flexible electronics, health monitoring, and IoT systems.
Synergistic Protection Mechanism of Underwater Welding Technology and Long-Term Anti-Corrosion Coatings for Marine Engineering
Marine structures are crucial for offshore energy production, transportation, and various industrial activities, but they are highly susceptible to corrosion, which significantly reduces their operational lifespan. While underwater welding and anti-corrosion coatings are commonly used individually to address these issues, there is a lack of research into integrating these two techniques for enhanced long-term protection. This study proposes a synergistic protection mechanism that combines underwater welding with advanced anti-corrosion coatings to improve corrosion resistance and structural integrity in marine environments. The proposed system was optimized using multi-objective genetic algorithms to balance welding parameters and coating properties. Experimental results show that the integrated system improves corrosion resistance by 30% compared to traditional welding methods and by 15% over existing systems. The synergy between welding and coating is verified through ablation studies, which show that the combined system offers superior protection, achieving a corrosion resistance of 0.2 g/cm², compared to 0.29 g/cm² for welding alone. This research provides a novel approach to marine corrosion protection, offering significant potential for reducing maintenance costs and extending the lifespan of underwater structures. The findings contribute to both academic knowledge on welding-coating integration and practical solutions for the marine industry, with implications for improving sustainability and reducing operational costs
Gold Nanoparticle-Based Optical Biosensing Platform for Ultrasensitive Glucose Monitoring
Accurate quantification of glucose is essential for diabetes management, particularly within low-concentration ranges where early hypoglycemia detection remains clinically challenging. Existing optical nanobiosensors frequently rely on single-channel readouts, resulting in baseline drift, limited sub-millimolar resolution, and inconsistent reproducibility across synthesis batches. To address these limitations, we developed a gold nanoparticle (AuNP)-based optical biosensing platform integrating dual-channel signal acquisition with glucose oxidase (GOx) chromogenic amplification and batch-normalized calibration. The system simultaneously records LSPR wavelength shift (Δλ) and scattering intensity ratio (I540/I620), improving sensitivity and measurement stability relative to single-mode detection. Across 1,200 serum samples (0-20 mM), the platform achieved a limit of detection of 18.6±2.3 µM, mean absolute error of 0.21±0.03 mM compared with clinical analyzers, and spectral linearity of R² = 0.98. Ablation testing confirmed functional necessity of the enzymatic amplification pathway (LoD increased to 78.6±7.9 µM when GOx was removed), while batch-calibration reduced inter-fabrication drift. Stability evaluation demonstrated minimal 14-day signal attenuation (4.3±0.5%) with <1.8 nm wavelength drift, and cross-institution validation showed no significant performance decline (p > 0.05). These results demonstrate a reproducible and sensitive optical route to glucose detection, with practical applicability to future point-of-care or minimally invasive monitoring frameworks.
Based on Deep Learning Intelligent Information Systems: Architecture, Knowledge Integration & Decision Optimization
The exponential growth of multi-source digital data in domains such as healthcare and manufacturing has intensified the need for intelligent information systems (IIS) capable of accurate, interpretable, and resource-efficient decision-making. However, existing deep learning-based IIS often suffer from fragmented knowledge integration, limited decision optimization, and inadequate explainability, restricting their generalization and compliance in real-world environments. This study proposes a deep learning-based IIS that unifies three core components: a Knowledge Integration Module (KIM) for semantic alignment of structured and unstructured data through graph-based fusion; a Hybrid Decision-Optimization Engine (HDOE) combining reinforcement learning and constrained optimization for adaptive decision control; and an Explainable Representation Layer (ERL) providing feature-level attribution to enhance transparency and auditability. Empirical evaluations on two public datasets, industrial and medical, demonstrate significant performance gains over four baselines: accuracy = 92.1±0.4 %, F1 = 91.7±0.5 %, and latency reduction = 18.7 %. Interpretability scores improved by 0.9 points, while cross-domain accuracy degradation remained under 5 % with noise-induced accuracy loss below 2.5 %. These results confirm that the proposed IIS achieves statistically verified improvements in efficiency, interpretability, and robustness. The framework provides a reproducible and explainable foundation for deep learning-based decision systems applicable to data-intensive, compliance-sensitive domains such as healthcare, finance, and industrial optimization.
One-Click Regression Benchmarking with SHAP Explainability: An Integrated Python Pipeline for Linear, Regularized, and Tree-Boosting Models
Applied researchers often need a fast, reproducible way to (a) compare multiple regression algorithms under a consistent preprocessing and evaluation protocol and (b) interpret model behavior beyond scalar accuracy metrics. This paper presents a turnkey Python pipeline that benchmarks five widely used regressors-Ordinary Least Squares, Ridge, Random Forests, XGBoost, and LightGBM-while natively integrating SHAP-based explainability. The system accepts mixed-type datasets, performs robust preprocessing (median imputation and standardization for numeric predictors; most-frequent imputation and one-hot encoding for categorical predictors), and evaluates models on a hold-out set using R², MSE, RMSE, and MAE. Results are exported to a clean, analysis-ready Excel workbook to facilitate immediate reuse in empirical reports. To move beyond aggregate metrics, the pipeline automatically generates SHAP global importance summaries (bar and beeswarm) and feature-dependence plots with interaction highlighting, providing multi-level insight into main effects and potential interactions. The implementation is designed for portability and minimal configuration: users specify a data file and target column, and optional flags control test split, random seed, and the number of visualizations. When no data are provided, a synthetic mixed-type dataset is generated to demonstrate the full workflow end-to-end. By combining standardized benchmarking with model-agnostic interpretability, the proposed tool lowers the barrier to rigorous, transparent model comparison and accelerates the translation of machine-learning methods into substantive research across domains.
Next-Generation Observatories: Probing the Universe Beyond the Visible Spectrum
Modern astronomy is rapidly evolving beyond the confines of the visible spectrum. While optical telescopes have historically shaped cosmic observation, they capture only a fraction of astrophysical activity. This study identifies a critical research gap: the absence of a systemic framework that unites multi-wavelength and multi-messenger observations into coherent scientific understanding. To address this, a comparative qualitative analysis was conducted across three representative observatories, the James Webb Space Telescope (JWST), the Square Kilometre Array (SKA), and the LIGO-Virgo-KAGRA network. Drawing on systems theory and epistemic coherence theory, the study constructs a triadic model encompassing technological synergy, temporal synchronization, and epistemic integration. Results show that cross-spectrum interoperability enhances resolution by approximately three- to fivefold and reduces interpretive uncertainty through multi-messenger validation. Beyond empirical improvement, the findings reveal a conceptual transformation: observation becomes a distributed, collaborative process of coherence rather than isolated detection. The research thus contributes both a theoretical framework, observational ecosystems, and a practical model for integrating heterogeneous observatories. It demonstrates that next-generation astronomical infrastructures function not merely as instruments of measurement but as infrastructures of understanding, fundamentally redefining how scientific knowledge of the universe is generated and verified.
Research on Co-Optimization of Control Logic and Structural Design for Intelligent MEMS via Digital Twin Technology
The performance of Micro-Electro-Mechanical Systems (MEMS) depends on the coordinated interaction between structural design and control logic. However, existing design methodologies treat these domains independently, lacking real-time feedback and multi-objective optimization. To address this limitation, this study proposes a digital-twin-driven co-optimization framework that integrates structural and control parameter tuning within a unified, synchronized environment. The framework combines finite-element modeling, real-time sensor feedback, and a hybrid evolutionary-gradient optimization algorithm to jointly minimize energy consumption, response delay, and resonance deviation under physical constraints. Experimental validation on a silicon-based micro-cantilever MEMS demonstrates a precision improvement of 4.6%, energy reduction of 18.7%, and response delay decrease of 23.5% compared to baseline methods. The framework achieved stable convergence within 150 epochs, with a 46% lower variance and performance retention above 95% across piezoelectric and thermal MEMS devices. SHAP-based interpretability analysis further revealed that stiffness, damping, and control gain jointly explain 63% of performance variance, confirming the physical consistency of the model. These results indicate that integrating digital twin synchronization with hybrid optimization provides a reproducible and interpretable pathway for intelligent MEMS co-design, enhancing precision, efficiency, and robustness across multi-physics operating conditions.
Lift-Drag Characteristics and Vortex Interaction Mechanisms of Two-Dimensional Tandem Airfoils
The aerodynamic performance of tandem airfoils arranged in fore-aft configurations has become an important subject in low Reynolds number aerodynamics, owing to its relevance for unmanned aerial vehicles, formation flight, and energy-efficient aircraft. While prior studies have clarified the influence of spacing and positioning on lift and drag, most have focused on time-averaged coefficients or steady conditions, leaving the dynamic relationship between vortex evolution and instantaneous aerodynamic forces insufficiently understood. This study employs large-scale transient numerical simulations to analyze two-dimensional tandem airfoils, with visualization supported by FieldView 20 and quantitative analysis performed in Tecplot. Aerodynamic coefficients, including lift, pressure drag, and viscous drag, were extracted for both fore and aft airfoils, and systematically correlated with transient vortex structures across representative time instants. The results demonstrate that the fore airfoil maintains quasi-steady performance with minimal coefficient fluctuations, serving as a stable aerodynamic baseline. By contrast, the aft airfoil exhibits strong unsteady oscillations in lift and drag, directly governed by vortex growth, detachment, and reattachment, as confirmed by surface pressure distributions and flow field contours. These findings bridge the gap between force production and vortex dynamics in tandem configurations, advancing theoretical understanding of unsteady aerodynamics and offering practical insights for UAV formation design and bio-inspired flight strategies.
Elemental Signatures of Xianfeng Tongbao: XPS Evidence of Fiscal Crisis and Currency Devaluation in Late Qing China
The Xianfeng reign (1851-1861) was marked by severe fiscal crisis and monetary instability in Qing China, when official and privately minted coins circulated simultaneously. While numismatic and historical studies have described this phenomenon, they often lack systematic material analysis, and scientific investigations of alloy composition rarely extend to socio-economic interpretation. This study bridges the gap by employing X-ray photoelectron spectroscopy (XPS) to compare the elemental composition of an official and a privately minted Xianfeng Tongbao coin. Surface cleaning, rust removal, and high-resolution XPS scans were conducted at three random points for each specimen, followed by CasaXPS peak fitting to quantify Cu, Zn, Pb, and Sn contents. Results show that official coins maintained high Cu content with stable Zn and Sn proportions, ensuring structural stability, whereas private issues exhibited reduced Cu and elevated Zn and Pb, with inconsistent Sn, leading to weaker alloys. These compositional differences directly reflect resource scarcity, fiscal weakness, and declining state control. The findings demonstrate that microstructural chemistry can serve as material evidence of macroeconomic crisis, contributing both to numismatic science and to economic history, while offering methodological value for archaeology and heritage conservation.
Improving Ionic Conductivity and Interface Stability in All-Solid-State Batteries Using Nanocomposite Solid Electrolytes
All-solid-state batteries (ASSBs) have emerged as promising candidates for next-generation energy storage due to their intrinsic safety and compatibility with high-energy-density lithium metal anodes. However, their commercialization is hindered by two persistent bottlenecks: the relatively low ionic conductivity of solid electrolytes compared to liquids and the instability of electrode-electrolyte interfaces. Existing approaches often improve one aspect in isolation but fail to simultaneously optimize both properties under practical cycling conditions. In this study, we propose an in situ-assembled nanocomposite solid electrolyte, where oxide nanoparticles are homogeneously dispersed within a sulfide-based host matrix through a sol-gel assisted synthesis. This design integrates percolation pathways that reduce activation energy for Li⁺ transport and introduces a nanoscale passivation layer that suppresses interfacial side reactions. Electrochemical tests demonstrate a room-temperature ionic conductivity of 1.7 × 10⁻³ S·cm⁻¹, a 2.3-fold improvement over pure sulfide and a 6.1-fold improvement over oxide baselines. Interfacial resistance was reduced to 18 Ω·cm², and full-cell cycling retained 91% capacity after 300 cycles. Ablation and robustness studies further confirm the critical role of filler concentration, particle size, and interfacial engineering. These results establish a reproducible framework for enhancing both conductivity and interfacial stability in ASSBs. The proposed nanocomposite design provides a scalable pathway toward safer, high-performance solid-state batteries, directly supporting future applications in electric vehicles and grid-scale energy storage.
Robust Evaluation of Vaccination Strategies under Epidemiological and Logistical Uncertainty
Infectious disease outbreaks present persistent challenges for public health policy, particularly when key factors such as transmission intensity, vaccine efficacy, and rollout logistics are uncertain. This study develops a stochastic Susceptible-Infectious-Recovered (SIR) framework to conduct a robust evaluation of vaccination strategies under joint epidemiological and logistical uncertainty. Randomness is incorporated into transmission, recovery, and vaccine uptake processes, enabling the model to reflect the variability of real-world epidemic dynamics. Monte Carlo simulations are performed to compare fixed-rate, phased, and threshold-triggered vaccination strategies across a wide range of plausible outbreak scenarios. Sensitivity analysis highlights the dominant influence of transmission rate and vaccine efficacy on epidemic outcomes, while a cost-effectiveness framework balances epidemiological benefits against resource constraints. Results indicate that adaptive, threshold-triggered vaccination strategies consistently reduce worst-case epidemic peaks and improve resilience under uncertainty. These findings provide actionable guidance for policymakers seeking robust, resource-efficient interventions in rapidly evolving epidemic contexts.
Efficient PageRank Computation on Large-Scale Graphs: A Fast-Track Optimization Framework
The explosive growth of large-scale networks such as the Web, Twitter, and Wikipedia has intensified the demand for efficient node-ranking algorithms. PageRank remains one of the most influential techniques for measuring node importance, yet traditional implementations face critical bottlenecks in scalability, convergence speed, and memory efficiency when applied to billion-scale graphs. Existing optimizations, ranging from sparse matrix compression to distributed computation and approximation, offer partial solutions but fail to deliver a unified balance between accuracy and performance. This study proposes a fast-track optimization framework for PageRank computation that integrates three complementary strategies: hierarchical sparse decomposition to reduce memory overhead, parallelized convergence acceleration with residual-based scheduling to improve scalability, and adaptive approximation to minimize redundant iterations under provable error bounds. The framework is implemented on heterogeneous platforms including GPUs and distributed clusters. Extensive experiments on WebGraph, Twitter, and Wikipedia demonstrate that the method reduces runtime by up to 45% and memory consumption by nearly 30% compared with state-of-the-art baselines, while maintaining Kendall's Tau accuracy above 0.96. Visualization confirms interpretability, and robustness tests validate stability under graph perturbations and dynamic updates. These results establish the framework as a scalable and reliable solution for real-time network analytics, search engines, and recommendation systems.
Applied Mathematics in the Construction of Quantitative Investment Strategies and Risk Assessment
The integration of applied mathematics into quantitative finance has enabled systematic portfolio construction and rigorous risk assessment, yet most traditional approaches overly rely on variance as a risk proxy and fail to capture asymmetric and tail-dependent dynamics. Despite significant advances in stochastic modeling and portfolio optimization, there remains insufficient unification between mathematical modeling, coherent risk measures, and empirical investment practices. To address this gap, this study develops a comprehensive framework for quantitative investment that combines stochastic processes, convex and robust optimization, and risk-adjusted evaluation using Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR), while conducting comparative experiments and analyses of naïve allocation, mean-variance optimization, and CVaR-adjusted models through case studies and simulations. The empirical results demonstrate that CVaR-based strategies outperform traditional mean-variance portfolios by achieving higher cumulative returns with superior downside protection, while robust optimization reduces drawdowns under market stress. This research advances the theoretical link between risk modeling and portfolio design and provides practical insights for institutional investors seeking resilient, mathematically grounded strategies in increasingly volatile financial markets.
A Privacy-Preserving Data Sharing Framework Based on Generative Adversarial Networks
This paper proposes a privacy-preserving data sharing framework based on Generative Adversarial Networks (GANs), integrating a multi-discriminator mechanism, a dynamic differential privacy adjustment strategy, and a controllable generation module. The framework aims to balance data utility and privacy protection across high-risk domains. In medical data sharing (MIMIC-III) and cross-institutional financial analysis, experiments show that the proposed approach outperforms standard GANs, Differential Privacy Logistic Regression, and Federated Learning in generation quality, downstream task performance, and resistance to inference attacks. The multi-discriminator design constrains the generator from statistical, semantic, and temporal perspectives to mitigate mode collapse, while the dynamic privacy strategy adapts noise levels during training to optimize the privacy-utility trade-off. The controllable generation module enables tailored data distributions for specific business needs, improving minority-class performance. Although the framework introduces computational overhead, it offers a viable solution for secure, high-quality data sharing. Future work will focus on lightweight architectures, automated parameter tuning, and multimodal, cross-domain extensions to enhance adaptability and scalability.