Mathematical Modeling and Algorithm Application
type: channel
description: Mathematical Modeling and Algorithm Application (MMAA) is a peer-reviewed journal published by Darcy & Roy Press. The mission of the journal is to provide an academic forum for discussing the issues of mathematical modeling, algorithm application, theoretical and applied mathematics across the world. All articles published are rigorously and fast reviewed meeting the Journal Quality standards.
publisher: Darcy & Roy Press
language: en
publication name: Mathematical Modeling and Algorithm Application
issn: 3006-2020
items: Synergy and Empowerment: A Study on the Internal Mechanisms of New Farmer Cultivation Promoting Rural Revitalization—A Case Study of Foshan
items: Jamming Missile Deployment Strategy Based on Simulated Annealing Algorithm and Exhaustive Search Method
items: Urban Facility Risk Assessment and Response Priority Decision-Making Based on Vision-Language Transformer
items: The Balance Between Economic Development and Environmental Protection: Sustainable Tourism in Juneau City
items: Research on the Application of Data Science in Traffic Optimization under the Background of Smart City
items: Research on the Optimization of UAV Smoke Screen Interference Bomb Delivery Strategy Based on Spatio - Temporal Motion Model
items: A Study on Spatio-Temporal Cooperative Optimization Model and Strategy for Single UAV Smokescreen Jamming
items: A Study on Predicting Short-Term Interest Behaviour of Social Media Users Based on Cosine Clustering and Sliding Window Logistic Regression
items: Study on Urban Logistics and Distribution Path and Carbon Emission Balance Based on Multi-objective Optimization
items: Predictive Study of Monthly Changes in Global Atmospheric Methane Based on Arimr Modeling
items: Research on Intelligent Segmentation Method of Oracle Bone Script Images Based on U-Net Model
items: Intelligent Optimization and Control Framework for Complex Dynamic Systems: Integrating Two-Tier Fuzzy Evaluation and Differential Nonlinear Optimization
items: Research on Innovative Teaching Strategies for Teachers Based on DeepSeek's Intelligent Support
items: Development of a Dual/Multi-Beam Interferometric Epitaxial Layer Thickness Calculation Model Integrating FFT and Least Squares Method
items: Research on an Improved YOLO Model-Based Algorithm for UAV Poppy Recognition
items: Cost Efficient Scaling Strategies for Large Language Models in Multi-Cloud Environment
items: Mathematical Modeling for Optimizing the Tilt Angle of Solar Panels
items: Multimodal Affective Computing Method with Cross-Modal Attention
items: Optimization of Multi-Factor NIPT Detection Time Points Based on Random Forest Feature Screening and K-Means Clustering
items: Research on Sustainable Development of Tourism Industry in Juneau Based on Nonlinear Optimization
items: Economic forecast of Guangdong-Hong Kong-Macao Greater Bay Area
items: Marine Chlorophyll Prediction Based on Gray Correlation Analysis and Random Forest Modeling
items: Power Iteration on Non-Normal Matrices: Spectral Radius, Jordan Effects, and Krylov-Based Accelerations
items: Large Language Models for Financial Knowledge Extraction Analytical Insights and Corporate Planning Support
items: Hybrid Architectures that Combine LLMs and Predictive Analytics for Next-Generation Financial Modeling
items: Intelligent Insurance Claims Management through AI and Automation
items: Research on the Urban Development Resilience and Sustainability Based on Data Analysis and Model Construction
items: Research on Mergers and Acquisitions of Listed Companies Based on Dictionary Method and Development of New Quality Productivity
items: Day-Ahead Photovoltaic Power Forecasting Using Multi-Source Data Fusion
items: Research on Heilongjiang Rice Industry Based on Multiple Linear Regression Analysis

Synergy and Empowerment: A Study on the Internal Mechanisms of New Farmer Cultivation Promoting Rural Revitalization—A Case Study of Foshan
type: item
description: The deep advancement of the rural revitalization strategy urgently requires cracking the talent bottleneck, and the "New Farmer" group is becoming a key variable in activating rural endogenous dynamics. Academic research on New Farmers has mostly focused on entrepreneurial performance or policy support, lacking a deep deconstruction of the "internal mechanisms" of how they dynamically interact with the rural system to achieve systemic transformation. Based on the theoretical perspective of "synergy-empowerment," this paper uses a combination of qualitative analysis and case study methods to clarify the dynamic coupling relationship between the two major systems of "New Farmer cultivation" and "rural revitalization." The study argues that New Farmer cultivation is not a traditional linear logic of "talent introduction" but a "two-way synergy" construction process. This mechanism manifests as follows: New Farmers, as agents, achieve dynamic coupling with the rural system through industrial synergy (value chain reconstruction), cultural synergy (local value activation), ecological synergy (ecological asset transformation), and organizational synergy (governance network innovation). Simultaneously, this synergistic process relies on the support system provided by policy empowerment (systemic ecosystem construction), digital empowerment (platform-based integration), and social empowerment (identity construction). This paper uses the practice of New Farmers in Foshan under the background of "urban-rural integration" as an illustration, analyzing how New Farmers, in a manufacturing highland and at the forefront of a metropolitan circle, leverage digital technology and market advantages to propel rural industries toward the high end of the value chain. The research concludes that future rural talent policies should shift from "fragmented subsidies" to "systemic empowerment," focusing on building an innovative and entrepreneurial ecosystem that supports the growth of New Far…
subject: Articles
  topic: section
subject: New Farmers; Rural Revitalization; Synergistic Mechanisms; Empowerment; Internal Mechanisms; Foshan
  topic: keywords
creator: Yingmei Li
creator: Fengqi Ye
creator: Dongjin He
rights: Copyright (c) 2025 Yingmei Li, Fengqi Ye, Dongjin He https://creativecommons.org/licenses/by-nc/4.0
date: 2025-11-28
publication date: 2025-11-28
starting page: 72
ending page: 77
doi: 10.54097/365fne58

Jamming Missile Deployment Strategy Based on Simulated Annealing Algorithm and Exhaustive Search Method
type: item
description: To enhance the battlefield survivability of personnel and equipment, smoke jamming shells have become one of the focuses of war preparedness research and development in various countries. Based on mechanism analysis at the kinematic level, simulated annealing algorithm under multi-round cyclic mechanism, and exhaustive method, this paper studies the deployment strategy of smoke jamming shells. Based on the motion models of jamming shells, cloud clusters, and missiles, this paper constructs a single-objective optimization mathematical function with the longest effective shielding time as the optimization goal. To obtain more accurate calculation results, this paper adopts the simulated annealing algorithm, which can achieve autonomous multi-round loop functionality, and ultimately derives the optimal effective shielding time.
subject: Articles
  topic: section
subject: Kinematics equation, simulated annealing algorithm, multi-round loop mechanism, exhaustive search method.
  topic: keywords
creator: Xiangpeng Wu
creator: Shuwen Yue
creator: Xuyuan Deng
rights: Copyright (c) 2025 Xiangpeng Wu, Shuwen Yue, Xuyuan Deng https://creativecommons.org/licenses/by-nc/4.0
date: 2025-11-28
publication date: 2025-11-28
starting page: 68
ending page: 71
doi: 10.54097/h5fpjp24

Urban Facility Risk Assessment and Response Priority Decision-Making Based on Vision-Language Transformer
type: item
description: This study addresses issues in urban management information systems to improve the intelligence level of urban facility risk identification and disposal priority decision. These issues include fragmented multi-source data, risk assessment relying on manual experience, and a lack of a quantitative basis for work order sorting. The study introduces Vision-Language Transformer (VLT) to construct an integrated multimodal framework for urban facility risk assessment and response priority decision-making. The framework takes road and municipal facility images, inspection records, and complaint work order texts as inputs. Through VLT, it fuses visual and linguistic features in a unified semantic space to obtain joint representations that can characterize both appearance defects and semantic hidden dangers simultaneously. Combined with business features such as location importance and historical failure frequency, it outputs risk scores and response priority rankings. Experiments select a public road damage dataset, dividing it into three subsets (A, B, C) corresponding to main road business districts, residential and public service facilities, and old areas. The optimized model of this study is compared with the Single-modal Vision Transformer-based Risk Assessment Framework (SVT-Risk) and the Early-Fusion Multimodal Transformer-based Risk Assessment Framework (EFMT-Risk). Results show that the accuracy of the optimized model in subsets A, B, and C is 0.91, 0.89, and 0.88; the mean average precision (MAP) in subsets A, B, and C reaches 0.87, 0.86, and 0.89, respectively. The overall performance is superior to the two comparison frameworks, indicating that the method can more accurately identify high-risk facilities and reasonably sort the disposal order; meanwhile, it can effectively alleviate the problems of inaccurate risk identification and unreasonable work order priority in traditional urban management information systems. Therefore, this study makes certain contribut…
subject: Articles
  topic: section
subject: Vision-Language Transformer; Urban facility risk assessment; Response priority decision-making; Multimodal deep learning; Urban management information system
  topic: keywords
creator: Xiaojun Li
creator: Chengjin Jiang
creator: Jinlu Sun
creator: Jiaqin Mo
creator: Jianjuan Fan
rights: Copyright (c) 2025 Xiaojun Li, Chengjin Jiang, Jinlu Sun, Jiaqin Mo, Jianjuan Fan https://creativecommons.org/licenses/by-nc/4.0
date: 2025-11-28
publication date: 2025-11-28
starting page: 63
ending page: 67
doi: 10.54097/j7bwmw51

The Balance Between Economic Development and Environmental Protection: Sustainable Tourism in Juneau City
type: item
description: To effectively balance economic gains and ecological conservation in tourism-intensive cities, this study examines Juneau, Alaska—a quintessential tourism-dependent city—and constructs a multi-objective optimization framework for sustainable tourism based on a stochastic movement model. By modeling the stochastic fluctuations of beer and housing prices and incorporating symbolic functions to quantify tourist behavior choices and local residents' response strategies, the framework establishes a constrained optimization model to systematically optimize overall social, economic, and environmental benefits. Key parameters were estimated using linear regression methods, combined with Monte Carlo simulation techniques to conduct stochastic simulations and evaluations of profit and sustainability indicators under various scenarios. Results indicate that setting hotel accommodation prices at $160 per night and limiting annual visitor numbers to approximately 15,800 can maximize regional profits while significantly reducing environmental pressure, thereby alleviating resource competition between local communities and external tourists. The modeling mechanism proposed in this study not only provides a scientific quantitative decision-making tool for the sustainable development of resource-constrained tourism cities but also holds significant theoretical value and practical guidance for advancing tourism policy design that balances equity and efficiency.
subject: Articles
  topic: section
subject: Tourism Economy, Sustainable Profit Optimization Model, Resilience Analysis, Regional Economy, Tourism Scale.
  topic: keywords
creator: Binhan Yang
creator: Qitong Gao
creator: Ziteng Wang
creator: Xuqi Cao
creator: Nana Hu
rights: Copyright (c) 2025 Binhan Yang, Qitong Gao, Ziteng Wang, Xuqi Cao, Nana Hu https://creativecommons.org/licenses/by-nc/4.0
date: 2025-11-28
publication date: 2025-11-28
starting page: 56
ending page: 62
doi: 10.54097/37f82n25

Research on the Application of Data Science in Traffic Optimization under the Background of Smart City
type: item
description: The rapid expansion of urban areas in China has exacerbated traffic congestion and environmental pressures, leading to the rise of smart city projects that apply data science techniques to optimize traffic systems. This study investigates the use of various data-driven approaches—including big data analytics, machine learning algorithms, and Internet of Things (IoT) frameworks—to improve traffic management in Chinese smart cities. It provides an overview of fundamental methods and real-world applications such as continuous traffic monitoring, predictive modeling of traffic flows, and the enhancement of public transportation services. Examples from metropolitan areas like Beijing and Shenzhen are presented to demonstrate practical outcomes. The discussion also covers key obstacles involving data security, system integration, infrastructure readiness, and the availability of skilled personnel. The paper concludes by identifying future avenues for research focusing on cutting-edge AI technologies, self-driving vehicles, governance of data, and active citizen involvement. The evidence indicates that leveraging data-centric solutions can substantially advance urban transportation efficiency, environmental sustainability, and residents’ overall living standards in China’s fast-developing cities.
subject: Articles
  topic: section
subject: Data Science; Traffic Optimization; Smart City; Big Data Analytics; Machine Learning.
  topic: keywords
creator: Qiman Yang
rights: Copyright (c) 2025 Qiman Yang https://creativecommons.org/licenses/by-nc/4.0
date: 2025-11-28
publication date: 2025-11-28
starting page: 50
ending page: 55
doi: 10.54097/fqg4qt32

Research on the Optimization of UAV Smoke Screen Interference Bomb Delivery Strategy Based on Spatio - Temporal Motion Model
type: item
description: This paper aims to optimize the coordinated masking tactics of UAV smoke screen interference bombs to maximize the effective masking time against incoming missiles. The research focuses on the strategy deployment problem of UAVs delivering smoke screen interference bombs and gradually constructs and solves a series of mathematical models. First, for the single - UAV - single - bomb scenario under fixed parameters, a spatio - temporal motion trajectory model of the missile, the UAV, and the smoke screen cloud is established. Through the line - of - sight blocking principle, an effective masking duration of 1.38 seconds is calculated, which lays a benchmark for subsequent optimizations. On this basis, the problem is transformed into a multi - dimensional nonlinear optimization problem. The flight angle, speed of the UAV, and the delivery and detonation times of the interference bombs are taken as decision variables, aiming to maximize the effective masking time. By using the genetic algorithm to conduct a global parameter scan under multiple physical and tactical constraints, the effective masking time is significantly increased to 4.58 seconds. Subsequently, to address the complexity of the coordinated delivery of multiple bombs (three interference bombs) by a single UAV, the optimization model framework is extended. More decision variables are introduced, and under the delivery interval constraints, the particle swarm optimization (PSO) algorithm is used to efficiently solve the high - dimensional non - convex problem. Finally, an effective masking duration of a total of 6.25 seconds is achieved.
subject: Articles
  topic: section
subject: Kinematic model, intelligent optimization algorithm, parameter optimization.
  topic: keywords
creator: Xuejun Yu
creator: Lingyue Fang
creator: Jialang Huang
rights: Copyright (c) 2025 Xuejun Yu, Lingyue Fang, Jialang Huang https://creativecommons.org/licenses/by-nc/4.0
date: 2025-11-28
publication date: 2025-11-28
starting page: 44
ending page: 49
doi: 10.54097/1e1m7f23

A Study on Spatio-Temporal Cooperative Optimization Model and Strategy for Single UAV Smokescreen Jamming
type: item
description: This study addresses the strategy optimization problem of single UAV platforms deploying smokescreen jamming bombs in complex confrontation scenarios. Firstly, by establishing a mathematical model of missile kinematics and the spatial geometric relationship of smokescreen cloud formations, the effective coverage time for a single bomb against a single target was precisely calculated, providing a benchmark for subsequent optimization. Secondly, a multi-variable constrained optimization model was constructed, with UAV flight direction, speed, and smokescreen bomb deployment and detonation parameters as decision variables, and a grid search algorithm was employed to maximize the coverage effectiveness of a single jamming bomb. Finally, for the case of a single UAV deploying multiple jamming bombs, a multi-bomb cooperative deployment strategy based on temporal planning and spatial coverage was designed. By optimizing the deployment sequence using a grid search algorithm, the overall coverage time was significantly extended. Research results indicate that through precise spatio-temporal cooperative optimization, the single-bomb coverage time can be increased from a baseline of 1.405 seconds to 4.2 seconds, and the total coverage time can be further extended to 4.62 seconds under multi-bomb cooperation. This study provides a quantitative and operable method for single UAV smokescreen jamming strategies, offering reference value for tactical decisions in the field of electronic countermeasures.
subject: Articles
  topic: section
subject: Smokescreen Jamming, Single UAV, Spatio-temporal Cooperative Optimization, Deployment Strategy, Coverage Effectiveness.
  topic: keywords
creator: Xin Feng
rights: Copyright (c) 2025 Xin Feng https://creativecommons.org/licenses/by-nc/4.0
date: 2025-11-28
publication date: 2025-11-28
starting page: 36
ending page: 43
doi: 10.54097/vksdvn96

A Study on Predicting Short-Term Interest Behaviour of Social Media Users Based on Cosine Clustering and Sliding Window Logistic Regression
type: item
description: In recent years, social media platforms have profoundly influenced human social interactions and information acquisition. Precise analysis of user needs enables efficient content matching, fostering positive ecosystem cycles that ultimately enhance platform competitiveness and commercial value. Addressing the requirement to "predict which users will gain new followers on 22 July" for a specific social media platform, this paper proposes a user-blogger interaction modelling approach integrating Dynamic Bayesian Network principles with collaborative filtering mechanisms. First, a normalised user-blogger interaction matrix is constructed, utilizing cosine similarity to partition users into three interest-homogeneous clusters. Subsequently, a three-day sliding window extracts daily totals of views, likes, and comments as short-term behavioural features. Independent binary Logistic regression models with L1 regularisation are trained for each cluster, addressing positive-negative sample imbalance through oversampling. Time-series cross-validation (training: 11–17 July; validation: 18–20 July) indicates optimal performance with a three-day window, achieving adjusted R² ≥ 0.84. The final model outputs the probability of all unfollowed bloggers following each other on 22 July, alongside a Top-3 recommendation. Taking U7 as an example, the probability of them following B7 reached 85.37%. Robustness and sensitivity tests demonstrate the model's sound stability and practical value for platform recommendation scenarios. This paper excels in dynamic data modelling, drawing extensively from literature to establish an optimal model with comprehensive consideration of the problem. Having passed robustness and sensitivity tests, the model holds reference value for platform push notifications.
subject: Articles
  topic: section
subject: User-blogger interaction, Dynamic Bayesian networks, Cosine similarity clustering, Sliding window features, Binary logistic regression.
  topic: keywords
creator: Junpeng Yuan
creator: Jingmin Lan
creator: Yiting Qiu
creator: Dang Gan
creator: Jiaxin Huang
creator: Yun Huang
creator: Nuo Chen
creator: Hang Zeng
creator: Yiweige Li
rights: Copyright (c) 2025 Junpeng Yuan, Jingmin Lan, Yiting Qiu, Dang Gan, Jiaxin Huang, Yun Huang, Nuo Chen, Hang Zeng, Yiweige Li https://creativecommons.org/licenses/by-nc/4.0
date: 2025-11-28
publication date: 2025-11-28
starting page: 33
ending page: 35
doi: 10.54097/883e3j76

Study on Urban Logistics and Distribution Path and Carbon Emission Balance Based on Multi-objective Optimization
type: item
description: With the growing demand of urban logistics and distribution, how to optimize the distribution path to minimize the cost and carbon emission has become an important issue. In this paper, a multi-objective optimization model is proposed for an urban logistics and distribution problem, aiming to balance the distribution cost and carbon emission. Specifically, this paper firstly proposes a static path optimization model to design the optimal distribution routes under fixed traffic conditions using the mixed integer linear programming (MILP) method. Second, Genetic Algorithm (GA) and Dynamic Programming (DP) are used for real-time distribution paths when dynamic traffic changes (e.g., changes in traffic flow during morning and evening peaks) are considered. Finally, for the trade-off relationship between distribution cost and carbon emission, this paper constructs a multi-objective optimization model and adopts the weighted sum method to balance these two objectives. By setting appropriate weight coefficients, this paper obtains multiple Pareto-optimal solutions and provides path selection schemes under different weight configurations. In the solution process, genetic algorithm and particle swarm optimization (PSO) are used to find the optimal distribution paths and combined with constrained optimization techniques to ensure that the path selection traffic rules and other constraints. The experimental results in this paper show that reasonable path selection can significantly carbon emissions while optimizing the distribution path. In addition, the dynamic traffic strategy considered in the model can also effectively deal with the traffic changes during peak hours. The research in this paper provides valuable theoretical support for the optimization of urban logistics and distribution paths, and provides concrete implementation solutions for practical applications such as balancing cost and environmental protection goals. The results show the trade-offs between different…
subject: Articles
  topic: section
subject: Urban Logistics, Path Optimization, Carbon Trade-off, Multi-objective Optimization, Genetic Algorithm.
  topic: keywords
creator: Zhaokun Li
rights: Copyright (c) 2025 Zhaokun Li https://creativecommons.org/licenses/by-nc/4.0
date: 2025-11-28
publication date: 2025-11-28
starting page: 26
ending page: 32
doi: 10.54097/jhz96m62

Predictive Study of Monthly Changes in Global Atmospheric Methane Based on Arimr Modeling
type: item
description: Atmospheric methane is the second most abundant greenhouse gas in the atmosphere after carbon dioxide, and its concentration changes significantly regulate the intensity of the greenhouse effect, thereby exerting a profound influence on global warming and related climate change. Accurately understanding the changing patterns and future trends of atmospheric methane concentrations is of critical importance for elucidating the mechanisms of climate change, developing scientifically sound and effective emission reduction strategies, and advancing the realization of the global carbon neutrality objective. This study aims to construct a statistical model using the ARIMR model by integrating the characteristics of autoregression (AR), moving average (MA), and regression (R) to conduct predictive analysis of monthly changes in global atmospheric methane concentrations. The research data are sourced from monitoring stations under the World Meteorological Organization (WMO) Global Atmospheric Watch (GAW) program, covering monthly atmospheric methane concentration observations from 1994 to 2021. First, the data were subjected to stationarity tests, with the ADF test used to determine stationarity, and non-stationary data were differenced. Subsequently, the autoregressive correlation function (ACF) plot and partial autoregressive correlation function (PACF) plot were used for model order determination, ultimately selecting the ARIMR(0,1,1) model for predictive analysis. The results indicate that the ARIMR(0,1,1) model with a drift term demonstrates good fitting performance and predictive capability when forecasting changes in atmospheric methane concentrations, with small prediction errors and the ability to effectively continue the original trend of the series. Using the ARIMR model with a drift term to forecast atmospheric methane concentrations for 2025, the results show that atmospheric methane concentrations are expected to continue rising in the future.
subject: Articles
  topic: section
subject: Atmospheric Methane, Monthly Variations, ARIMR Model, Global Climate Change.
  topic: keywords
creator: Haomiao Wang
creator: Shixian Deng
creator: Zixuan He
rights: Copyright (c) 2025 Haomiao Wang, Shixian Deng, Zixuan He https://creativecommons.org/licenses/by-nc/4.0
date: 2025-11-28
publication date: 2025-11-28
starting page: 18
ending page: 25
doi: 10.54097/d40cxr57

Research on Intelligent Segmentation Method of Oracle Bone Script Images Based on U-Net Model
type: item
description: As a precious data for the study of Chinese civilization, Oracle bone inscriptions are difficult to identify due to problems such as rubings, contamination and blur. Although the U-Net deep learning model can effectively retain image details, it still faces challenges such as low contrast, many noise points, complex glyphes and limited data sets when dealing with Oracle bone inscriptions. Gray-scale and binarization processing simplifies the image structure and highlights the text area. At the same time, the mean filter is superior to the Gaussian filter in removing noise, and the PSNR is improved by 15%. The Canny edge detection algorithm achieves an accuracy of 92.3% and effectively removes more than 85% of the interference noise under the optimized parameters. Based on the preprocessing results, a segmentation method based on the U-Net deep learning model is proposed. The model achieves 98.5% pixel classification accuracy on the training set, 89.7% mean intersection over Union (MIU) on the test set, and 91.2% F1 score. It is significantly better than the traditional segmentation method.
subject: Articles
  topic: section
subject: U-Net Deep Learning Model, Mean Filtering, Canny Edge Detection
  topic: keywords
creator: Zhike Zhang
rights: Copyright (c) 2025 Zhike Zhang https://creativecommons.org/licenses/by-nc/4.0
date: 2025-11-28
publication date: 2025-11-28
starting page: 13
ending page: 17
doi: 10.54097/m3mc9e43

Intelligent Optimization and Control Framework for Complex Dynamic Systems: Integrating Two-Tier Fuzzy Evaluation and Differential Nonlinear Optimization
type: item
description: The optimal state regulation of highly complex and uncertain dynamic systems under multi-objective and multi-constraint conditions has emerged as a critical research area in the field of intelligent optimization. Addressing the limitations of existing models in uncertainty representation, nonlinear dynamic modeling, and global optimization capabilities, this paper proposes an intelligent optimal control framework that integrates a two-tier fuzzy comprehensive evaluation model with a differential equation-based nonlinear optimal control algorithm. This framework employs a hierarchical fuzzy decision-making mechanism to precisely quantify multidimensional uncertainties and conflicting interests within complex systems. Furthermore, it achieves high-precision dynamic control of the system state by combining a first-order differential dynamic model with advanced nonlinear optimization strategies. The results demonstrate that the proposed algorithm significantly outperforms mainstream methods in terms of control accuracy, energy efficiency, and robustness, showcasing its exceptional adaptability and practical engineering value. Future work will focus on incorporating deep learning and reinforcement learning models to further enhance the algorithm's level of intelligence and generalization capabilities.
subject: Articles
  topic: section
subject: Intelligent Optimization, Adaptive Control, Nonlinear Optimization, Robustness Analysis, Fuzzy Comprehensive Evaluation
  topic: keywords
creator: Chen'an Zhou
creator: Jinyan Li
creator: Xun Luo
rights: Copyright (c) 2025 Chen'an Zhou, Jinyan Li, Xun Luo https://creativecommons.org/licenses/by-nc/4.0
date: 2025-11-28
publication date: 2025-11-28
starting page: 6
ending page: 12
doi: 10.54097/gmr0jt03

Research on Innovative Teaching Strategies for Teachers Based on DeepSeek's Intelligent Support
type: item
description: In the wave of digital education, DeepSeek intelligent tool promotes the innovation of education field. Taking the teaching of basic inequalities as an example, this paper aims to discuss how DeepSeek empowers teachers' teaching strategies and improves teaching quality through accurate integration of teaching resources, personalized teaching methods and real-time optimization of the teaching process. Meanwhile, this paper focuses on the two-way innovation of “teaching” and “learning”, on the one hand, it assists teachers to realize the transformation of innovative teaching from “one-way instillation” to “precise guidance”, and on the other hand, it helps teachers to realize the transformation of innovative teaching from “one-way instillation” to “precise guidance”. On the one hand, it assists teachers to realize the innovative teaching transformation from “one-way instillation” to “precise guidance”, and on the other hand, it promotes students' learning transformation from “passive acceptance” to “active inquiry”, so as to improve students' logical reasoning and problem solving abilities, and to realize the in-depth cultivation of core mathematical literacy.
subject: Articles
  topic: section
subject: Basic inequalities, Teaching strategies, Two-way innovation, Transformation
  topic: keywords
creator: Junpeng Yuan
creator: Ying Tan
creator: Yun Huang
creator: Yuexin Sun
creator: Hao Yin
rights: Copyright (c) 2025 Junpeng Yuan, Ying Tan, Yun Huang, Yuexin Sun, Hao Yin https://creativecommons.org/licenses/by-nc/4.0
date: 2025-11-28
publication date: 2025-11-28
starting page: 1
ending page: 5
doi: 10.54097/9h00zb38

Development of a Dual/Multi-Beam Interferometric Epitaxial Layer Thickness Calculation Model Integrating FFT and Least Squares Method
type: item
description: This paper proposes methods related to epitaxial layer thickness detection and inversion calculation, focusing on the exploration of epitaxial layer thickness calculation models based on the interference principle and the application of corresponding algorithms. Firstly, a specific dispersion model is adopted to fit the refractive index of the epitaxial layer, and an appropriate error metric is used to evaluate the fitting degree. Combined with Snell's law and Fresnel equations, the reflection and transmission coefficients are calculated. The optical path difference and total phase difference under variable refractive index are derived through integration, thereby establishing a thickness measurement model. Secondly, the Fast Fourier Transform (FFT) is utilized to obtain the initial thickness value. Combined with a weighted joint least squares optimization model, a specific algorithm combination is employed to invert the thickness. A resampling method is used to determine the confidence interval, ensuring a good fitting effect. Finally, the necessary conditions for multi-beam interference and the reflectivity formula are derived, and a multi-beam thickness calculation model is established. This model has a better goodness of fit and can correct the deviations of the previous model. Integrating multiple algorithms, the proposed model balances accuracy and efficiency, exhibits excellent fitting performance and strong robustness, and can accurately realize the detection of epitaxial layer thickness.
subject: Articles
  topic: section
subject: Fast Fourier Transform, Least Squares Method, Double-Beam Interference.
  topic: keywords
creator: Lei Sun
creator: Dongrui Lai
creator: Chenyue Zhang
creator: Xianwang Zhang
rights: Copyright (c) 2025 Lei Sun, Dongrui Lai, Chenyue Zhang, Xianwang Zhang https://creativecommons.org/licenses/by-nc/4.0
date: 2025-10-20
publication date: 2025-10-20
starting page: 121
ending page: 126
doi: 10.54097/gpa66r76

Research on an Improved YOLO Model-Based Algorithm for UAV Poppy Recognition
type: item
description: To address the challenges faced in drone-based poppy detection tasks, for instance small size, confusing backgrounds, and high recognition accuracy requirements, this paper come up with a poppy small target recognition method based on enhanced YOLOv9. Initially, the CAM unit was added to the backbone network, which enhances the ability of model to express fine-grained features of poppies by integrating Channel Attention and spatial Mixing mechanisms. Secondly, a content-guided attention model is incorporated into the neck of the feature pyramid, leveraging deep semantic information to adaptively weight features of different scales. This enhancement leads to an improvement in multifaceted feature integration and a substantial enhancement in the responsiveness of the detection system to diminutive objects. Finally, the issue of existing object detection loss functions not being sufficiently sensitive to bounding box regression is addressed by the introduction of the Inner IoU loss function. Finally, the issue of existing object detection loss functions not being sufficiently sensitive to bounding box regression is addressed by the creation of the Inner IoU loss as a solution. The latter focuses more on maximizing the overlap between the actual box and the predicted box, which can further improve the accuracy of position regression. Experiments conducted on a dataset of aerial photographs of poppies demonstrate that the designed algorithm enhances mAP by 13.5% and detection accuracy by 16.8% in comparison with the original YOLOv9 model. While ensuring real-time processing, it delivers higher recognition accuracy and stronger robustness, providing an effective solution for rapid and accurate poppy identification in drug control operations. Ensuring real-time processing, it delivers higher recognition accuracy and stronger robustness, offering an adequate means by which to swiftly and accurately identify poppies in drug control operations.
subject: Articles
  topic: section
subject: Poppy detection; YOLOv9; Loss function; Small target detection; Recognition accuracy
  topic: keywords
creator: Hu Li
creator: Yushuang Feng
creator: Jiajun Su
creator: Bo Yuan
rights: Copyright (c) 2025 Hu Li, Yushuang Feng, Jiajun Su, Bo Yuan https://creativecommons.org/licenses/by-nc/4.0
date: 2025-10-20
publication date: 2025-10-20
starting page: 111
ending page: 120
doi: 10.54097/x9yjy202

Cost Efficient Scaling Strategies for Large Language Models in Multi-Cloud Environment
type: item
description: The proliferation of large language models (LLMs) has intensified the need to utilize large quantities of computational resources, which causes the cost of operation and infrastructure issues to grow. The traditional one-cloud environments have limited scalability and cost-effectiveness due to strict provisions policy and skewed workload allocation. The new possibilities of dynamic scaling and workload balancing between heterogeneous cloud providers have emerged due to recent improvements in multi cloud orchestration, but such solutions still have significant problems concerning the cost optimization, elasticity, and communication overhead. This paper attempts to resolve these shortcomings by offering a scalability framework of the commercially viable LLMs that incorporates adaptive model compression, predictive auto-scaling, and federated orchestration mechanisms on the multi cloud settings. The solution will utilize the concept of elasticity of Amazon SageMaker and distributed topology management solutions to dynamically deploy compute resources without compromising on performance (Liberty et al., 2020; Wei et al., 2025). Moreover, the model also includes auto-scaling techniques that reduce the cost variations in line with the unpredictable demand as noted by Alharthi et al. (2024). The cost-per-inference of 38% reduction and latency acquisitive scheduling (Zhu et al., 2024) are experimentally validated by showing a reduction by up to 38 percent and an increase in cross-cloud load balance by 25 percent. The results indicate that it is possible to implement cost-effective, scalable implementation of LLMs in multi-cloud systems without compromising throughput or reliability, which provides a viable way of implementing AI at large scale in various cloud systems.
subject: Articles
  topic: section
subject: Cost Optimization; Multi-Cloud Deployment; Large Language Models; Auto-Scaling; Model Compression; Federated Learning; Edge Intelligence; Pipeline Parallelism; Elasticity; Trustworthy AI
  topic: keywords
creator: Xiuyuan Zhao
creator: Yao Ge
creator: Haijian Zhang
rights: Copyright (c) 2025 Xiuyuan Zhao, Yao Ge, Haijian Zhang https://creativecommons.org/licenses/by-nc/4.0
date: 2025-10-20
publication date: 2025-10-20
starting page: 101
ending page: 110
doi: 10.54097/3k9w7484

Mathematical Modeling for Optimizing the Tilt Angle of Solar Panels
type: item
description: Solar panels are more efficient when angled to maximize sunlight exposure. This paper investigates simple mathematical models to optimize the tilt angle, increasing energy production by 10–20%. Based on simple variables like the latitude and the seasonal position of the sun, these models then calculate the best tilts for projects as varied as powering a community center. Approaches use geometrical formulas and sunshine means validated by measurements and simulation. Difficulties such as clouds or dust are treated with pragmatic strategies. This concise guide aiding application of tilt optimization methods for efficient solar energy is the green technology of tomorrow. The research makes modelling through to real conditions manageable, to help optimize green power choices.
subject: Articles
  topic: section
subject: Solar Panels; Tilt Angle Optimization; Mathematical Modelling; Renewable Energy
  topic: keywords
creator: Enzhao Wang
rights: Copyright (c) 2025 Enzhao Wang https://creativecommons.org/licenses/by-nc/4.0
date: 2025-10-20
publication date: 2025-10-20
starting page: 97
ending page: 100
doi: 10.54097/aqzxbd87

Multimodal Affective Computing Method with Cross-Modal Attention
type: item
description: Multimodal affective computing (MAC) aims to enable machines to recognize and understand human emotions by integrating information from multiple modalities, including text, speech, facial expressions, and gestures. Traditional fusion methods such as early fusion, late fusion, and tensor fusion often struggle to capture fine-grained inter-modal dependencies and handle conflicts between modalities. Cross-modal attention has emerged as a powerful mechanism to address these challenges by selectively aligning and weighting features across modalities, highlighting relevant cues while suppressing noise. This survey reviews recent advances in MAC with a focus on cross-modal attention, covering core tasks such as Multimodal Sentiment Analysis (MSA), Multimodal Emotion Recognition in Conversations (MERC), and Multimodal Sarcasm and Humor Detection (MSD). We analyze the principles of cross-modal attention, summarize representative models, and discuss their empirical performance. Finally, we identify current challenges, including dataset limitations, asynchronous modality alignment, and interpretability, and propose future directions such as large-scale pretraining, knowledge-enhanced modeling, and interpretable attention mechanisms. Overall, cross-modal attention significantly improves robustness, context-awareness, and fine-grained emotion understanding in multimodal affective computing.
subject: Articles
  topic: section
subject: Multimodal Affective Computing, Cross-Modal Attention, Sentiment Analysis, Emotion Recognition in Conversation, Sarcasm Detection
  topic: keywords
creator: Bo He
creator: Hao Han
creator: Hongqian Zhang
creator: Weining Bai
rights: Copyright (c) 2025 Bo He, Hao Han, Hongqian Zhang, Weining Bai https://creativecommons.org/licenses/by-nc/4.0
date: 2025-10-20
publication date: 2025-10-20
starting page: 91
ending page: 96
doi: 10.54097/jcw9e446

Optimization of Multi-Factor NIPT Detection Time Points Based on Random Forest Feature Screening and K-Means Clustering
type: item
description: Aiming at the problems that multiple factors (BMI, age, etc.) synergistically affect the detection time point in non-invasive prenatal testing (NIPT) and the accuracy of the existing single-factor optimization schemes is insufficient, this paper proposes a multi-factor optimization scheme of "random forest feature screening - K-means clustering - risk - compliance rate constraint". The study was based on the NIPT data of male fetuses from the prenatal diagnosis center of a tertiary hospital. After preprocessing (cleaning abnormal samples, standardizing gestational weeks format, and calculating derived indicators), random forest was used to screen the key factors affecting the attainment of Y chromosome concentration standards, and then multi-factor homogeneous grouping was achieved through K-means clustering. Finally, the optimal detection time points for each group were screened in combination with the principle of "compliance rate ≥85% and lowest risk", and the impact of detection errors was analyzed. The results show that random forest effectively identifies the key influencing factors and eliminates the interference of redundant variables. K-means achieves efficient grouping. The compliance rate of the optimal detection time points in each group meets the clinical requirements, and the time deviation is small when the detection error is ≤3%. This scheme can precisely match the differences of multiple factors, solve the problems of feature redundancy and inefficient grouping, and provide reliable decision support for the timing planning of NIPT clinical testing.
subject: Articles
  topic: section
subject: Random Forest, K-Means Clustering, NIPT, Multi-Factor Optimization, Detection Time Point.
  topic: keywords
creator: Xuejian Liu
creator: Yifan Xue
creator: Wenwu Deng
rights: Copyright (c) 2025 Xuejian Liu, Yifan Xue, Wenwu Deng https://creativecommons.org/licenses/by-nc/4.0
date: 2025-10-20
publication date: 2025-10-20
starting page: 87
ending page: 90
doi: 10.54097/7qefvd77

Research on Sustainable Development of Tourism Industry in Juneau Based on Nonlinear Optimization
type: item
description: This study constructs a comprehensive mathematical model to assess the current state of tourism development in Juneau and propose sustainable optimization strategies. The model integrates key factors such as visitor numbers, carbon emissions, infrastructure pressure, and resident satisfaction. Aiming to maximize economic benefits and minimize environmental and social costs, it incorporates a feedback mechanism to characterize the dynamic redistribution of tourism revenue across infrastructure, environmental protection, and community welfare investments. Sensitivity analysis verifies the robustness of the model. The model is further extended to overtourism cities such as Xi'an. Using the analytic hierarchy process (AHP) to identify core influencing factors, a strategy for attracting less popular attractions based on dynamic pricing, facility improvements, and transportation optimization is proposed. Finally, multi-objective coordinated optimization is achieved by adjusting weights and pricing parameters. Prediction results indicate that this strategy can increase tourism revenue by approximately 20% while reducing carbon emissions by approximately 15%, providing a quantifiable and scalable decision-making framework for addressing overtourism and achieving sustainable urban tourism development.
subject: Articles
  topic: section
subject: Nonlinear optimization, feedback mechanism, sensitivity analysis, Analytic Hierarchy Process, dynamic pricing strategy
  topic: keywords
creator: Tianshu Wang
creator: Jiajun Li
creator: Wenshuo Zhang
creator: Xiaxia Cui
rights: Copyright (c) 2025 Tianshu Wang, Jiajun Li, Wenshuo Zhang, Xiaxia Cui https://creativecommons.org/licenses/by-nc/4.0
date: 2025-10-20
publication date: 2025-10-20
starting page: 81
ending page: 86
doi: 10.54097/c20s0b28

Economic forecast of Guangdong-Hong Kong-Macao Greater Bay Area
type: item
description: This study presents an innovative framework for analyzing and forecasting economic development in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), a key driver of China's economic growth. The research makes three significant contributions: First, it develops a novel multi-method analytical approach combining multiple linear regression, Granger causality tests, and principal component analysis (PCA) to systematically identify the most influential growth factors - particularly highlighting logistics networks and technological inputs as critical leverage points for policy intervention. Second, the study pioneers a hybrid modeling strategy that integrates ARIMA and LSTM techniques, demonstrating superior predictive performance for the GBA's 5-10 year economic trajectory. Third, the findings provide actionable insights for policymakers, suggesting that targeted investments in technology infrastructure and logistics optimization can accelerate regional development while offering a replicable model for other emerging mega-regions. The proposed framework not only enhances understanding of the GBA's growth dynamics but also establishes a new benchmark for regional economic analysis.
subject: Articles
  topic: section
subject: Principal Component Analysis, Arima Model, Lstm Model, Multiple Linear Regression.
  topic: keywords
creator: Jintian Zhang
creator: Yongtong Guan
creator: Jingqi Zhao
creator: Dianjun Xu
creator: Shaoyi Wu
rights: Copyright (c) 2025 Jintian Zhang, Yongtong Guan, Jingqi Zhao, Dianjun Xu, Shaoyi Wu https://creativecommons.org/licenses/by-nc/4.0
date: 2025-10-20
publication date: 2025-10-20
starting page: 75
ending page: 80
doi: 10.54097/shg06y43

Marine Chlorophyll Prediction Based on Gray Correlation Analysis and Random Forest Modeling
type: item
description: This study investigates the relationship between marine chlorophyll content and environmental factors using ocean observation big data. Through data preprocessing, gray correlation analysis, Spearman correlation analysis, and random forest modeling, the research identifies key environmental drivers of chlorophyll content, including carbon dioxide levels, seawater temperature, salinity, pH, and dissolved oxygen. The results reveal significant negative correlations between carbon dioxide and temperature with chlorophyll content, underscoring the potential role of marine chlorophyll in carbon sequestration. This work enhances understanding of marine ecosystem dynamics and provides a scientific basis for marine resource management and ecological conservation. By integrating multiple analytical methods, the study offers a novel approach for predicting marine chlorophyll content, contributing to global carbon cycle research and supporting sustainable marine resource management under environmental change.
subject: Articles
  topic: section
subject: Marine Chlorophyll, Grey Correlation Analysis, Random Forest Modeling.
  topic: keywords
creator: Xiaoyi Liu
rights: Copyright (c) 2025 Xiaoyi Liu https://creativecommons.org/licenses/by-nc/4.0
date: 2025-10-20
publication date: 2025-10-20
starting page: 68
ending page: 74
doi: 10.54097/x2hpgs53

Power Iteration on Non-Normal Matrices: Spectral Radius, Jordan Effects, and Krylov-Based Accelerations
type: item
description: The power iteration method is a foundational algorithm for approximating the dominant eigenvalue and eigenvector of matrices, widely applied in large-scale computations such as Google’s PageRank. While its convergence is well-understood for symmetric matrices, asymmetric (non-Hermitian) matrices present more complex challenges, influenced by the spectral radius and matrix structure. This paper investigates the mathematical relationship between the spectral radius and the convergence rate of power iteration in asymmetric matrices, focusing on both diagonalizable and non-diagonalizable cases. In diagonalizable matrices, convergence depends exponentially on the ratio r=|λ_2 |/|λ_1 |, where λ_1 and λ_2 are the dominant and subdominant eigenvalues. For non-diagonalizable matrices with Jordan blocks, additional polynomial factors k^(m-1) (where m is the block size) slow convergence, even with favorable eigenvalue gaps. Theoretical derivations, including matrix power computations in Jordan form, illustrate these effects. To address limitations such as slow convergence and inefficient information utilization, this paper extends the analysis to advanced iterative methods. Krylov subspace methods overcome single-vector restrictions by constructing optimal approximations in growing subspaces. The Arnoldi process generates orthogonal bases for non-symmetric matrices, leading to Hessenberg projections and Ritz approximations. Shift-and-invert techniques, based on spectral transformation theorems, accelerate convergence and target interior eigenvalues by manipulating the spectrum. Rayleigh quotient iteration introduces adaptive shifting, achieving cubic convergence for symmetric cases and quadratic for asymmetric. Subspace iteration generalizes to multiple eigenvalues using QR decomposition for block processing. Numerical experiments in Python compare basic power iteration with these extensions, demonstrating superior convergence in challenging scenarios (e.g., near-unity ratios …
subject: Articles
  topic: section
subject: Power Iteration, Spectral Radius, Jordan Blocks, Krylov Subspace, Arnoldi Process, Shift-and-Invert, Rayleigh Quotient Iteration, Subspace Iteration
  topic: keywords
creator: Jiaqian Zhang
rights: Copyright (c) 2025 Jiaqian Zhang https://creativecommons.org/licenses/by-nc/4.0
date: 2025-10-20
publication date: 2025-10-20
starting page: 57
ending page: 67
doi: 10.54097/81613487

Large Language Models for Financial Knowledge Extraction Analytical Insights and Corporate Planning Support
type: item
description: Large language models (LLMs) have emerged as transformative technologies in financial services, demonstrating unprecedented capabilities in extracting structured knowledge from unstructured financial documents, generating analytical insights, and supporting strategic corporate planning decisions. This review paper examines the comprehensive applications of LLMs including GPT-4, Claude, PaLM, and domain-specific financial models in automating knowledge extraction from diverse sources including earnings calls, financial reports, regulatory filings, and market commentary. We analyze how transformer-based architectures (TA) leverage attention mechanisms and contextual embeddings to understand complex financial terminology, temporal relationships, and causal connections in financial narratives. The paper explores advanced techniques including prompt engineering, few-shot learning, retrieval-augmented generation (RAG), and fine-tuning strategies that adapt general-purpose LLMs to specialized financial tasks. We examine applications in sentiment analysis of financial texts, automatic summarization of lengthy reports, entity recognition for companies and products, relationship extraction between financial events, and question-answering systems for financial queries. The review investigates how LLMs generate analytical insights through scenario analysis, trend identification, risk assessment, and competitive intelligence synthesis. We analyze corporate planning support applications including strategic initiative identification, market opportunity analysis, resource allocation recommendations, and investment thesis generation. Furthermore, we discuss integration architectures combining LLMs with structured databases, time-series models, and visualization tools to create comprehensive decision support systems. The paper addresses critical challenges including hallucination mitigation, accuracy verification, regulatory compliance, data privacy concerns, and the need for human o…
subject: Articles
  topic: section
subject:
  topic: keywords
  value: Large Language Models; Financial Knowledge Extraction; Natural Language Processing; Transformer Architecture; Corporate Planning; Sentiment Analysis; Question Answering; Retrieval-Augmented Generation; Financial Analytics; Strategic Decision Support
creator: Xuguang Zhang
creator: Mengdie Wang
rights: Copyright (c) 2025 Xuguang Zhang, Mengdie Wang https://creativecommons.org/licenses/by-nc/4.0
date: 2025-10-20
publication date: 2025-10-20
starting page: 44
ending page: 56
doi: 10.54097/7am6vk38

Hybrid Architectures that Combine LLMs and Predictive Analytics for Next-Generation Financial Modeling
type: item
description: The convergence of large language models (LLMs) and predictive analytics represents a transformative paradigm shift in financial modeling, offering unprecedented capabilities for processing multimodal data and generating actionable insights. This review examines the evolution, architecture, and applications of hybrid systems that integrate LLMs with traditional predictive models to address complex challenges in financial forecasting, risk management, and portfolio optimization. Recent advances in natural language processing (NLP) have enabled LLMs to extract nuanced sentiment and contextual information from vast textual datasets, while deep learning (DL) architectures such as long short-term memory (LSTM), gated recurrent units (GRU), and transformer-based models have demonstrated superior performance in capturing temporal dependencies within financial time series. The integration of these technologies through early, intermediate, and late fusion strategies has yielded hybrid architectures that leverage the complementary strengths of linguistic understanding and numerical prediction. This paper synthesizes current research on financial LLMs including BloombergGPT and FinGPT, explores attention mechanisms and multimodal data fusion techniques, and evaluates the application of these hybrid systems across sentiment analysis, stock prediction, portfolio management, and fraud detection. Critical challenges including explainability, regulatory compliance, computational efficiency, and data quality are examined alongside emerging solutions. The review concludes that hybrid architectures combining LLMs and predictive analytics represent the future of financial modeling, offering enhanced accuracy, interpretability, and adaptability to dynamic market conditions, while emphasizing the need for continued research in model transparency, ethical AI deployment, and standardized evaluation frameworks.
subject: Articles
  topic: section
subject:
  topic: keywords
  value: Large Language Models, Predictive Analytics, Hybrid Architectures, Financial Forecasting, Transformer Models, Multimodal Fusion, Sentiment Analysis, Explainable AI, Deep Learning, Portfolio Optimization
creator: Shiyang Chen
creator: Shaochen Ren
creator: Qun Zhang
rights: Copyright (c) 2025 Shiyang Chen, Shaochen Ren, Qun Zhang https://creativecommons.org/licenses/by-nc/4.0
date: 2025-10-20
publication date: 2025-10-20
starting page: 31
ending page: 43
doi: 10.54097/j1cjb453

Intelligent Insurance Claims Management through AI and Automation
type: item
description: The insurance industry faces mounting pressure to modernize claims management processes amid rising claim volumes, increasing operational costs, and evolving customer expectations. Traditional manual claims processing systems suffer from inefficiencies, high error rates, and extended processing times that negatively impact both operational performance and customer satisfaction. Artificial intelligence (AI) and automation technologies have emerged as transformative solutions, offering unprecedented capabilities to streamline claims workflows, enhance decision accuracy, and improve customer experiences. This comprehensive review examines the application of AI and automation in insurance claims management, analyzing research published between 2019 and 2025. We explore diverse AI technologies including machine learning (ML), deep learning (DL), natural language processing (NLP), computer vision, and robotic process automation (RPA) across various insurance domains. The review reveals that AI-powered systems achieve substantial improvements in processing efficiency, with automation reducing processing times by up to 80% while maintaining or improving accuracy. Deep learning architectures demonstrate particular effectiveness in complex tasks such as fraud detection, damage assessment, and claim severity prediction. However, significant challenges persist, including data quality concerns, integration complexities with legacy systems, model interpretability requirements, and ethical considerations regarding algorithmic decision-making. We identify emerging trends including explainable AI frameworks, federated learning for privacy preservation, and hybrid human-AI collaboration models. This review contributes to understanding the current state and future trajectory of intelligent claims management systems while highlighting critical implementation considerations for insurance organizations.
subject: Articles
  topic: section
subject: Artificial Intelligence, Insurance, Robotic Process Automation, Natural Language Processing, Fraud Detection
  topic: keywords
creator: Tiejiang Sun
creator: Xuguang Zhang
creator: Mengdie Wang
rights: Copyright (c) 2025 Tiejiang Sun, Xuguang Zhang, Mengdie Wang https://creativecommons.org/licenses/by-nc/4.0
date: 2025-10-20
publication date: 2025-10-20
starting page: 20
ending page: 30
doi: 10.54097/egkzgw98

Research on the Urban Development Resilience and Sustainability Based on Data Analysis and Model Construction
type: item
description: This paper constructs a mathematical model based on the analysis of Point of Interest (POI) information and real estate sales data, aiming to provide scientific guidance for the managers and decision-makers of City 1 and City 2, and assist them in achieving high-quality sustainable development under multiple challenges. Regarding the problems of housing price prediction and housing stock estimation, in the context of a downward trend in housing prices, five indicators such as the greening rate, the ratio of building area to parking space, and parking space management fees are constructed. The results show that there are no significant differences in the number of households, the number of parking spaces, and underground parking fees between the two cities.
subject: Articles
  topic: section
subject: Analytic Hierarchy Process (AHP); Sustainable Development; Resilience of Urban Development.
  topic: keywords
creator: Junpeng Yuan
creator: Hao Yin
creator: Jiongyi He
creator: Lin Zhang
creator: Yantong Li
rights: Copyright (c) 2025 Junpeng Yuan, Hao Yin, Jiongyi He, Lin Zhang, Yantong Li https://creativecommons.org/licenses/by-nc/4.0
date: 2025-10-20
publication date: 2025-10-20
starting page: 15
ending page: 19
doi: 10.54097/3kk1z184

Research on Mergers and Acquisitions of Listed Companies Based on Dictionary Method and Development of New Quality Productivity
type: item
description: In the context of high-quality economic development, new productive forces have become a key force driving industrial upgrading and economic growth. The impact of mergers and acquisitions (M&A) on the development of new productive forces in listed companies has attracted significant attention. This paper uses M&A events of A-share listed companies from 2015 to 2023 as a sample, employing the text dictionary method to construct a dictionary of new productive forces, assessing the level of new productive forces in enterprises, and using the multi-period difference-in-differences method to investigate the impact of M&A on their development. The study finds that M&A significantly enhances the level of new productive forces in enterprises, with more pronounced positive effects on larger companies and those that have merged two roles into one, while having a negative effect on older companies with concentrated equity. Further heterogeneity tests show that M&A has a more significant promoting effect on new productive forces in state-owned enterprises, high-tech industry companies, and regions with well-developed factor markets. Mechanism analysis confirms that M&A promotes the development of new productive forces by enhancing human capital levels and R&D intensity. The study provides reference for corporate strategic decision-making and government policy formulation. Based on the above research, this paper recommends precise targeting of M&A to enhance new productive forces; formulating differentiated policies to support M&A upgrades; expanding research dimensions to delve deeper into the mechanisms of M&A impacts, thereby improving the service efficiency of M&A for new productive force enterprises.
subject: Articles
  topic: section
subject: Mergers and Acquisitions of Listed Companies, New Quality Productivity, Text Dictionary Method, Multi-period Double Difference Method.
  topic: keywords
creator: Yihe Wang
creator: Jingfang Cui
rights: Copyright (c) 2025 Yihe Wang, Jingfang Cui https://creativecommons.org/licenses/by-nc/4.0
date: 2025-10-09
publication date: 2025-10-09
starting page: 125
ending page: 130
doi: 10.54097/1rejg923

Day-Ahead Photovoltaic Power Forecasting Using Multi-Source Data Fusion
type: item
description: This study proposes a day-ahead forecasting framework for photovoltaic power generation, integrating numerical weather prediction and deep learning to enhance prediction accuracy and optimize grid scheduling. Using 15-minute resolution data from August 2018 to June 2019, a comprehensive model was developed to analyze PV power characteristics, extracting seasonal and diurnal patterns via solar irradiance theory and Fast Fourier Transform. An Attention-based Long Short-Term Memory model, combined with NWP data, predicts 24-hour power curves with a mean absolute error of 5.2 kW. Further enhancements using Convolutional Neural Networks and Bidirectional LSTM, alongside spatial downscaling with XGBoost and Kriging interpolation, improve accuracy in complex terrains, reducing wind speed prediction MAE by 69.93%. The framework performs robustly across sunny, cloudy, and extreme weather scenarios, offering reliable support for grid operations and renewable energy integration.
subject: Articles
  topic: section
subject: Photovoltaic Power, Day-Ahead Forecasting, Numerical Weather Prediction, Spatial Downscaling, Deep Learning.
  topic: keywords
creator: Maoyuan Li
creator: Sudi Xu
creator: Mijia Song
rights: Copyright (c) 2025 Maoyuan Li, Sudi Xu, Mijia Song https://creativecommons.org/licenses/by-nc/4.0
date: 2025-10-09
publication date: 2025-10-09
starting page: 187
ending page: 191
doi: 10.54097/1554g234

Research on Heilongjiang Rice Industry Based on Multiple Linear Regression Analysis
type: item
description: Against the backdrop of a severe global food security situation, accurate prediction of crop yields is crucial for ensuring stable food supply and maintaining socio-economic stability. Heilongjiang Province is an important region for rice production in China, and its rice yield is of great significance to the stable supply of national grain. This study uses multiple linear regression and random forest regression models to conduct in-depth research and predictive analysis on rice production in various cities of Heilongjiang Province. The data is sourced from authoritative databases and field research, after data cleaning, which includes removing outliers, handling missing values, and correcting errors, and standardization, which normalizes data to a common scale (such as using z-score normalization or min-max scaling) to ensure consistency and comparability, models are constructed. The multiple linear regression model is based on the assumption of linear relationship, estimates parameters using the least squares method, and rigorously tests and evaluates them; The random forest regression model, as an optimization model, utilizes multiple decision trees for ensemble learning to improve prediction accuracy and reliability. Comparing the predicted values of the two models, it was found that they each have their own advantages, providing diverse and powerful support for predicting rice yield in Heilongjiang. This not only contributes to the scientific planning and sustainable development of the rice industry in Heilongjiang Province, but also has profound significance for ensuring national food security and promoting the modernization of agriculture.
subject: Articles
  topic: section
subject: Multiple Linear Regression, Model Testing, Error Analysis, Random Forest Regression
  topic: keywords
creator: Rui Yang
creator: Junqian Shao
creator: Xueyan Song
creator: Zidan Wang
rights: Copyright (c) 2025 Rui Yang, Junqian Shao, Xueyan Song, Zidan Wang https://creativecommons.org/licenses/by-nc/4.0
date: 2025-10-09
publication date: 2025-10-09
starting page: 183
ending page: 186
doi: 10.54097/pc44xs21