Mathematical Modeling and Algorithm Application
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.
Synergy and Empowerment: A Study on the Internal Mechanisms of New Farmer Cultivation Promoting Rural Revitalization—A Case Study of Foshan
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 Farmers, thereby achieving a synchronized resonance between talent cultivation and comprehensive rural revitalization.
New Farmers; Rural Revitalization; Synergistic Mechanisms; Empowerment; Internal Mechanisms; Foshan
Copyright (c) 2025 Yingmei Li, Fengqi Ye, Dongjin He https://creativecommons.org/licenses/by-nc/4.0
Jamming Missile Deployment Strategy Based on Simulated Annealing Algorithm and Exhaustive Search Method
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.
Kinematics equation, simulated annealing algorithm, multi-round loop mechanism, exhaustive search method.
Copyright (c) 2025 Xiangpeng Wu, Shuwen Yue, Xuyuan Deng https://creativecommons.org/licenses/by-nc/4.0
Urban Facility Risk Assessment and Response Priority Decision-Making Based on Vision-Language Transformer
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 contributions to the field of urban facility risk assessment and intelligent urban management information system construction.
Vision-Language Transformer; Urban facility risk assessment; Response priority decision-making; Multimodal deep learning; Urban management information system
Copyright (c) 2025 Xiaojun Li, Chengjin Jiang, Jinlu Sun, Jiaqin Mo, Jianjuan Fan https://creativecommons.org/licenses/by-nc/4.0
The Balance Between Economic Development and Environmental Protection: Sustainable Tourism in Juneau City
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.
Tourism Economy, Sustainable Profit Optimization Model, Resilience Analysis, Regional Economy, Tourism Scale.
Copyright (c) 2025 Binhan Yang, Qitong Gao, Ziteng Wang, Xuqi Cao, Nana Hu https://creativecommons.org/licenses/by-nc/4.0
Research on the Application of Data Science in Traffic Optimization under the Background of Smart City
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.
Data Science; Traffic Optimization; Smart City; Big Data Analytics; Machine Learning.
Copyright (c) 2025 Qiman Yang https://creativecommons.org/licenses/by-nc/4.0
Research on the Optimization of UAV Smoke Screen Interference Bomb Delivery Strategy Based on Spatio - Temporal Motion Model
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.
Kinematic model, intelligent optimization algorithm, parameter optimization.
Copyright (c) 2025 Xuejun Yu, Lingyue Fang, Jialang Huang https://creativecommons.org/licenses/by-nc/4.0
A Study on Spatio-Temporal Cooperative Optimization Model and Strategy for Single UAV Smokescreen Jamming
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.
Smokescreen Jamming, Single UAV, Spatio-temporal Cooperative Optimization, Deployment Strategy, Coverage Effectiveness.
Copyright (c) 2025 Xin Feng https://creativecommons.org/licenses/by-nc/4.0
A Study on Predicting Short-Term Interest Behaviour of Social Media Users Based on Cosine Clustering and Sliding Window Logistic Regression
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.
User-blogger interaction, Dynamic Bayesian networks, Cosine similarity clustering, Sliding window features, Binary logistic regression.
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
Study on Urban Logistics and Distribution Path and Carbon Emission Balance Based on Multi-objective Optimization
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 paths, which can provide decision support for logistics companies in multi-objective optimization and have strong practical application value.
Urban Logistics, Path Optimization, Carbon Trade-off, Multi-objective Optimization, Genetic Algorithm.
Copyright (c) 2025 Zhaokun Li https://creativecommons.org/licenses/by-nc/4.0
Predictive Study of Monthly Changes in Global Atmospheric Methane Based on Arimr Modeling
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.
Atmospheric Methane, Monthly Variations, ARIMR Model, Global Climate Change.
Copyright (c) 2025 Haomiao Wang, Shixian Deng, Zixuan He https://creativecommons.org/licenses/by-nc/4.0
Research on Intelligent Segmentation Method of Oracle Bone Script Images Based on U-Net Model
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.
U-Net Deep Learning Model, Mean Filtering, Canny Edge Detection
Copyright (c) 2025 Zhike Zhang https://creativecommons.org/licenses/by-nc/4.0
Intelligent Optimization and Control Framework for Complex Dynamic Systems: Integrating Two-Tier Fuzzy Evaluation and Differential Nonlinear Optimization
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.
Intelligent Optimization, Adaptive Control, Nonlinear Optimization, Robustness Analysis, Fuzzy Comprehensive Evaluation
Copyright (c) 2025 Chen'an Zhou, Jinyan Li, Xun Luo https://creativecommons.org/licenses/by-nc/4.0
Research on Innovative Teaching Strategies for Teachers Based on DeepSeek's Intelligent Support
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.
Basic inequalities, Teaching strategies, Two-way innovation, Transformation
Copyright (c) 2025 Junpeng Yuan, Ying Tan, Yun Huang, Yuexin Sun, Hao Yin https://creativecommons.org/licenses/by-nc/4.0
Development of a Dual/Multi-Beam Interferometric Epitaxial Layer Thickness Calculation Model Integrating FFT and Least Squares Method
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.
Fast Fourier Transform, Least Squares Method, Double-Beam Interference.
Copyright (c) 2025 Lei Sun, Dongrui Lai, Chenyue Zhang, Xianwang Zhang https://creativecommons.org/licenses/by-nc/4.0
Research on an Improved YOLO Model-Based Algorithm for UAV Poppy Recognition
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.
Poppy detection; YOLOv9; Loss function; Small target detection; Recognition accuracy
Copyright (c) 2025 Hu Li, Yushuang Feng, Jiajun Su, Bo Yuan https://creativecommons.org/licenses/by-nc/4.0
Cost Efficient Scaling Strategies for Large Language Models in Multi-Cloud Environment
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.
Cost Optimization; Multi-Cloud Deployment; Large Language Models; Auto-Scaling; Model Compression; Federated Learning; Edge Intelligence; Pipeline Parallelism; Elasticity; Trustworthy AI
Copyright (c) 2025 Xiuyuan Zhao, Yao Ge, Haijian Zhang https://creativecommons.org/licenses/by-nc/4.0
Mathematical Modeling for Optimizing the Tilt Angle of Solar Panels
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.
Solar Panels; Tilt Angle Optimization; Mathematical Modelling; Renewable Energy
Copyright (c) 2025 Enzhao Wang https://creativecommons.org/licenses/by-nc/4.0
Multimodal Affective Computing Method with Cross-Modal Attention
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.
Multimodal Affective Computing, Cross-Modal Attention, Sentiment Analysis, Emotion Recognition in Conversation, Sarcasm Detection
Copyright (c) 2025 Bo He, Hao Han, Hongqian Zhang, Weining Bai https://creativecommons.org/licenses/by-nc/4.0
Optimization of Multi-Factor NIPT Detection Time Points Based on Random Forest Feature Screening and K-Means Clustering
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.
Random Forest, K-Means Clustering, NIPT, Multi-Factor Optimization, Detection Time Point.
Copyright (c) 2025 Xuejian Liu, Yifan Xue, Wenwu Deng https://creativecommons.org/licenses/by-nc/4.0
Research on Sustainable Development of Tourism Industry in Juneau Based on Nonlinear Optimization
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.
Nonlinear optimization, feedback mechanism, sensitivity analysis, Analytic Hierarchy Process, dynamic pricing strategy
Copyright (c) 2025 Tianshu Wang, Jiajun Li, Wenshuo Zhang, Xiaxia Cui https://creativecommons.org/licenses/by-nc/4.0
Economic forecast of Guangdong-Hong Kong-Macao Greater Bay Area
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.
Principal Component Analysis, Arima Model, Lstm Model, Multiple Linear Regression.
Copyright (c) 2025 Jintian Zhang, Yongtong Guan, Jingqi Zhao, Dianjun Xu, Shaoyi Wu https://creativecommons.org/licenses/by-nc/4.0
Marine Chlorophyll Prediction Based on Gray Correlation Analysis and Random Forest Modeling
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.
Marine Chlorophyll, Grey Correlation Analysis, Random Forest Modeling.
Copyright (c) 2025 Xiaoyi Liu https://creativecommons.org/licenses/by-nc/4.0
Power Iteration on Non-Normal Matrices: Spectral Radius, Jordan Effects, and Krylov-Based Accelerations
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 or large Jordan blocks). Results highlight how these methods expand applicability in fields like Markov chains and control theory. This study underscores the need for structural awareness in eigenvalue computations and provides a framework for selecting appropriate extensions, enhancing efficiency in practical asymmetric matrix problems.
Power Iteration, Spectral Radius, Jordan Blocks, Krylov Subspace, Arnoldi Process, Shift-and-Invert, Rayleigh Quotient Iteration, Subspace Iteration
Copyright (c) 2025 Jiaqian Zhang https://creativecommons.org/licenses/by-nc/4.0
Large Language Models for Financial Knowledge Extraction Analytical Insights and Corporate Planning Support
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 oversight in high-stakes financial decisions. We examine evaluation methodologies for financial LLM applications, including domain-specific benchmarks, expert assessment protocols, and real-world performance metrics. Through synthesis of current research and deployed systems, we identify limitations including computational costs, update frequency challenges, bias in training data, and difficulties in explaining model reasoning. The review concludes by outlining promising research directions including multimodal financial analysis, real-time information integration, federated learning for privacy-preserving collaboration, and neuro-symbolic approaches combining neural language understanding with formal financial reasoning.
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
Copyright (c) 2025 Xuguang Zhang, Mengdie Wang https://creativecommons.org/licenses/by-nc/4.0
Hybrid Architectures that Combine LLMs and Predictive Analytics for Next-Generation Financial Modeling
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.
Large Language Models, Predictive Analytics, Hybrid Architectures, Financial Forecasting, Transformer Models, Multimodal Fusion, Sentiment Analysis, Explainable AI, Deep Learning, Portfolio Optimization
Copyright (c) 2025 Shiyang Chen, Shaochen Ren, Qun Zhang https://creativecommons.org/licenses/by-nc/4.0
Intelligent Insurance Claims Management through AI and Automation
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.
Artificial Intelligence, Insurance, Robotic Process Automation, Natural Language Processing, Fraud Detection
Copyright (c) 2025 Tiejiang Sun, Xuguang Zhang, Mengdie Wang https://creativecommons.org/licenses/by-nc/4.0
Research on the Urban Development Resilience and Sustainability Based on Data Analysis and Model Construction
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.
Analytic Hierarchy Process (AHP); Sustainable Development; Resilience of Urban Development.
Copyright (c) 2025 Junpeng Yuan, Hao Yin, Jiongyi He, Lin Zhang, Yantong Li https://creativecommons.org/licenses/by-nc/4.0
Research on Mergers and Acquisitions of Listed Companies Based on Dictionary Method and Development of New Quality Productivity
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.
Mergers and Acquisitions of Listed Companies, New Quality Productivity, Text Dictionary Method, Multi-period Double Difference Method.
Copyright (c) 2025 Yihe Wang, Jingfang Cui https://creativecommons.org/licenses/by-nc/4.0
Day-Ahead Photovoltaic Power Forecasting Using Multi-Source Data Fusion
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.
Photovoltaic Power, Day-Ahead Forecasting, Numerical Weather Prediction, Spatial Downscaling, Deep Learning.
Copyright (c) 2025 Maoyuan Li, Sudi Xu, Mijia Song https://creativecommons.org/licenses/by-nc/4.0
Research on Heilongjiang Rice Industry Based on Multiple Linear Regression Analysis
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.
Multiple Linear Regression, Model Testing, Error Analysis, Random Forest Regression
Copyright (c) 2025 Rui Yang, Junqian Shao, Xueyan Song, Zidan Wang https://creativecommons.org/licenses/by-nc/4.0