Multi-factor Model Research Articles

Spatiotemporal Dynamics and Driving Mechanism of Aboveground Biomass Across Three Alpine Grasslands in Central Asia over the Past 20 Years Using Three Algorithms

Aboveground biomass (AGB) is a sensitive indicator of grassland resource quality and ecological degradation. However, accurately estimating AGB at large scales to reveal long-term trends remains challenging. Here, single-factor parametric models, multi-factor parametric models, and multi-factor non-parametric models (Random Forest) were developed for three grassland types (alpine meadow, alpine grassland, and swampy meadow) in the Bayanbuluk Grassland using MODIS satellite data and environmental factors, including climate and topography. A 10-fold cross-validation method was employed to assess the accuracy and stability of these models, and an AGB remote sensing inversion model was established to estimate the AGB of the Bayanbuluk Grassland from 2005 to 2024. Moreover, the BEAST mutation test, Theil–Sen median trend analysis, and Mann–Kendall test were used to analyse the temporal trends of AGB, identify the years of mutation points, and explore the changes in AGB across the entire study period (2005–2024) and at 5-year intervals, considering the influence of climatic factors. The results indicated that the machine learning (RF) model outperformed both multi-factor parametric and single-factor parametric models, with specific improvements in R2 and RMSE across all grassland types. For instance, the RF model achieved an R2 of 0.802 in alpine grasslands, outperforming the multi-factor parametric model with an R2 of 0.531. The overall spatial distribution of AGB exhibited heterogeneity, with a gradual increase from northwest to southeast over the study period. Interannual AGB changes fluctuated significantly, with an overall increasing trend. Notably, from 2015 to 2019, 78% of the Bayanbuluk Grassland area showed a nonsignificant increase in AGB. Specifically, 46.7% of the alpine meadow AGB, 23% of the alpine grassland AGB, and 8.3% of the swampy meadow AGB showed non-significant increases. Further, temperature was found to be the dominant driver of AGB, with a stronger effect on alpine meadows and alpine grasslands than on swampy meadows. This is likely due to the relatively constant moisture levels in the swampy meadows, where precipitation plays a more prominent role. This study provides a comprehensive assessment of AGB trends, including both spatial and temporal analyses, which will inform future grassland resource management.

Corrigendum to “A Multi-factor model for predicting cement setting time” [J. Build. Eng. (98) (2024), 1–22/110991

Corrigendum to “A Multi-factor model for predicting cement setting time” [J. Build. Eng. (98) (2024), 1–22/110991

Food Insecurity Is Common in the Orthopaedic Trauma Population.

Food insecurity is the condition of limited access to healthy and safe food. Malnutrition resulting from food insecurity is a concern particularly in the surgical population due to the association with impaired healing. This aim of this study was to report the incidence and risk factors for food insecurity in the orthopaedic trauma population. Orthopaedic trauma centers at three distinct regions of the United States enrolled patients who had undergone extremity or pelvis fracture fixation within the previous 6 months. Participants completed the United States Department of Agriculture Household Food Insecurity Survey, and food insecurity was defined as a score ≥3. In addition, participants recorded patient demographics and injury/treatment/household characteristics and completed information about diet quality. Diet quality was compared between households with and without food insecurity using chi-square or Fisher exact tests. Logistic regression was used to create a multivariable model of factors associated with greater odds of food insecurity. Food insecurity was documented in 11.5% (81/703) of households. Households with food insecurity were less likely to report daily consumption of fruit, vegetables, and protein-rich foods. We found a greater odds of food insecurity among households with a yearly income of <$50,000 (odds ratio = 4.30 [95% confidence interval = 2.07 to 8.92], P < 0.001), tobacco use (2.33 [1.26 to 4.28], P = 0.007), Medicaid or no insurance (2.34 [1.19 to 4.62], P = 0.014), and Hispanic or Latino ethnicity (4.55 [1.69 to 12.24], P = 0.003), for each 10-year decrease in age (1.19 [1.00 to 1.40], P = 0.045), multiple surgically treated fractures (2.41 [1.08 to 5.35], P = 0.031), and for each additional 15 minutes of travel time to the nearest grocery store (2.12 [1.37 to 3.26], P < 0.001). Food insecurity is common in the orthopaedic trauma population, and households with food insecurity are more likely to have low diet quality. Nutrition supplementation during the healing phase after trauma and referral to nutrition assistance programs has the potential to mitigate malnutrition and prevent negative outcomes resulting from food insecurity.

Machine Learning-Based predictive model for adolescent metabolic syndrome: Utilizing data from NHANES 2007–2016

Metabolic syndrome (Mets) in adolescents is a growing public health issue linked to obesity, hypertension, and insulin resistance, increasing risks of cardiovascular disease and mental health problems. Early detection and intervention are crucial but often hindered by complex diagnostic requirements. This study aims to develop a predictive model using NHANES data, excluding biochemical indicators, to provide a simple, cost-effective tool for large-scale, non-medical screening and early prevention of adolescent MetS. After excluding adolescents with missing diagnostic variables, the dataset included 2,459 adolescents via NHANES data from 2007–2016. We used LASSO regression and 20-fold cross-validation to screen for the variables with the greatest predictive value. The dataset was divided into training and validation sets in a 7:3 ratio, and SMOTE was used to expand the training set with a ratio of 1:1. Based on the training set, we built eight machine learning models and a multifactor logistic regression model, evaluating nine predictive models in total. After evaluating all models using the confusion matrix, calibration curves and decision curves, the LGB model had the best predictive performance, with an AUC of 0.969, a Youden index of 0.923, accuracy of 0.978, F1 score of 0.989, and Kappa value of 0.800. We further interpreted the LGB model using SHAP, the SHAP hive plot showed that the predictor variables were, in descending order of importance, BMI age sex-specific percentage, weight, upper arm circumference, thigh length, and race. Finally, we deployed it online for broader accessibility. The predictive models we developed and validated demonstrated high performance, making them suitable for large-scale, non-medical primary screening and early warning of adolescent Metabolic syndrome. The online deployment of the model allows for practical use in community and school settings, promoting early intervention and public health improvement.

The association of systemic immune-inflammation index with incident breast cancer and all-cause mortality: evidence from a large population-based study

BackgroundChronic low-grade inflammation is recognized as a significant factor in various health outcomes, including the development and progression of breast cancer. The Systemic Immune-Inflammation Index (SII), a novel marker derived from routine blood counts, has been suggested as a predictor of all-cause mortality and cardiovascular mortality. However, its predictive value in a nationwide representative population, particularly for breast cancer incidence and mortality, is not well-established.MethodsThis study aimed to assess the association of SII and the risk of breast cancer incidence and all-cause mortality in breast cancer patients within the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018. SII was calculated from complete blood count parameters. We used multifactor regression models to examine the associations between SII and the outcomes of interest.ResultsA total of 21,058 female participants were included in the study, of which 557 (2.7%) were identified as having breast cancer. After adjusting for multiple potential confounders, the relationship between SII and the incidence of breast cancer revealed an inverse L-shaped association. The optimal inflection point for SII/100 was determined to be 5.09. Below this threshold, there was a significant increase in the risk of breast cancer (OR=1.05, 95% CI: 1.02-1.09). Within the breast cancer population, SII exhibited a J-shaped relationship with all-cause mortality. The optimal inflection point for SII/100 in this context was 5.22, and above this threshold, there was a marked escalation in all-cause mortality (HR=1.09, 95% CI: 1.04-1.14).ConclusionThe SII, as a novel inflammatory composite index, is significantly associated with the risk of breast cancer incidence and all-cause mortality in breast cancer patients. These findings highlight the importance of monitoring systemic inflammation and suggest that SII could serve as a valuable prognostic tool.

Tumor aggression-defense index–a novel indicator to predicts recurrence and survival in stage II-III colorectal cancer

BackgroundAlthough the TNM staging system plays a critical role in guiding adjuvant chemotherapy for colorectal cancer (CRC), its precision for risk stratification in stage II and III CRC patients with proficient DNA mismatch repair (pMMR) remains limited. Therefore, precise predictive models and research on postoperative treatments are crucial for enhancing patient survival and improving quality of life.MethodsThis retrospective study analyzed 1051 pMMR CRC patients who underwent radical resection and were randomly assigned to training (n = 736) and validation (n = 315) groups. Immunohistochemistry and hematoxylin and eosin staining were utilized to evaluate regulatory-Immunoscore (RIS), tertiary lymphoid structures (TLS), and tumor budding (TB). The Tumor Aggression-Defense Index (TADI) was derived through a multi-factor COX regression model. Subgroup analysis demonstrated potential of TADI in guiding personalized adjuvant therapy for stage II and III CRC.ResultsUnivariate and multivariate Cox analysis indicated that TADI was an independent prognostic indicator. Among stage II CRC, chemotherapy was significantly correlated with improved recurrence times in individuals with intermediate (95% CI 0.19–0.59, P < 0.001) and high (95% CI 0.36–0.95, P = 0.031) TADI. In stage III CRC receiving adjuvant chemotherapy, a duration of 3 months or longer was notably associated with a prolonged time to recurrence in those with high TADI (95% CI 0.40–0.98, P = 0.041) compared to durations of less than 3 months.ConclusionThe TADI serves as an effective parameter for predicting the survival outcomes of stage I-III pMMR CRC patients and guiding precision treatment strategies.

ESG as risk factor

Abstract There are numerous risk factors in asset pricing models that have been identified over the years. In this paper, we address the question of whether factors constructed using ESG (Environmental, Social, Governance) scores could potentially meet the necessary requirements for risk factors in multifactor models. While numerous studies indicate that the ESG performance of firms could be financially material, the integration of ESG factors has so far not been fully evaluated. We pay particular attention to the problem of divergent scores across different rating providers and investigate whether the regression results of 4- and 5-factor models converge. The evaluation is carried out with Fama–French and Carhart models, extended by an additional factor representing ESG, respectively. We find that there are ESG factors across all investigated rating providers that capture common-variation in stock returns over time, indicating that ESG should be considered in common asset pricing models.

The Impact of Inflation on the U.S. Stock Market After the COVID-19 Pandemic

Inflation remained quiescent for several decades and then surged in 2021 and 2022. Inflation subsequently fell in 2023 and 2024. This paper investigates how the rise and fall in inflation after 2019 affected the U.S. stock market. To do this, it estimates a fully specified multi-factor model that measures the exposure of 54 assets to inflation, monetary policy, and other macroeconomic variables over the 1994 to 2019 period. This paper then uses the inflation betas to investigate how investors’ perceptions of inflation changed between 2020 and 2024. The results indicate that concerns about inflation roiled the stock market over this period. The Fed’s anti-inflationary policies whipsawed markets even more. These findings highlight the dangers that arise when monetary policy allows inflation to accelerate.

A laser tracking attitude dynamic measurement method based on real-time calibration and AEKF with a multi-factor influence analysis model

Abstract To address the limitations of six-degree-of-freedom (6-DOF) visual tracking systems in terms of low dynamic performance and the inability to perform measurements under light occlusion, this paper proposes a novel laser tracking attitude dynamic measurement method integrating vision and inertial measurement unit (IMU). A tailored real-time calibration model is developed to enable precise alignment of the vision and IMU coordinate systems during dynamic operations. To overcome the limitations of extended Kalman filter (EKF) that rely on static noise assumptions in complex dynamic measurement scenarios, this study proposes an adaptive mechanism based on multi-factor influence analysis. Through dynamic monitoring of visual observation noise, an adaptive EKF algorithm is designed to enhance performance under varying conditions. When integrated into a vision/IMU fusion system, the algorithm significantly enhances the system’s sensitivity to variations in visual sensor observation noise caused by changes in lighting and motion states. This capability allows rapid adjustment of filtering parameters, leading to substantial improvements in filtering accuracy and stability. Simulation results demonstrate that the proposed algorithm outperforms traditional EKF in fusion measurement accuracy when tested in simulated noise environments featuring sinusoidal and random jump signals. Furthermore, the fusion measurement accuracy is mainly influenced by the data density of the visual sensor and the measurement distance. Finally, the proposed method was validated on a precision turntable. Experimental results reveal that at a 3 m measurement distance and within a 0°–25° angular range, when the turntable rotates at a constant speed of 5°/s along a single axis, the maximum absolute deviation in repeatability of the fused attitude measurements was below 0.11°, and the system is capable of achieving a high measurement frequency of 100 Hz.

Assessment of Environmental Suitability for the Spatial Distribution of Minority Villages in Mountainous Areas—Taking Fujian Province as an Example

The geographical environment and economic conditions that influence the spatial distribution of minority villages vary significantly, particularly in mountainous regions where they face different levels of disaster risks. Environmental suitability assessment can help us gain a deeper understanding of the differences and degrees of differences among different villages and provide a basis for resource allocation and planning. This study focuses on 499 national level and 68 provincial level minority villages in Fujian province and evaluates the environmental suitability of their spatial distribution using analysis tools such as ArcMap and GeoDetector. The research findings indicated: (1) The ratio between the influence strengths of natural geographical, socioeconomic, and disaster constraint indicators on the environmental suitability of minority villages in mountainous areas is 5:10:7, with the highest influence being the regional gross domestic product (RGDP) in the socioeconomic indicator (0.509) and the lowest influence being flood sensitivity (0.011). (2) Areas with high suitability values are in the southeast coastal and central regions. From east to west, the suitability values gradually decrease. The suitable areas cover 87.2% of the total region, including 91.9% of the minority villages. (3) The geographical correlation coefficient between the spatial distribution of minority villages and environmental suitability is 86.87, indicating a high degree of spatial interaction and fit between the two. This validates the rationality of the multi-factor evaluation model for assessing the environmental suitability of ethnic village spatial distribution in mountainous areas. (4) There is an inverse “U” relationship between environmental suitability and the distribution of national and provincial level minority villages. With the increase in the suitability index, the number of national level minority villages tends to increase, while the number of provincial level minority villages tends to decrease. The research findings have significant implications for promoting the sustainable development of minority villages in Fujian province and provide reference for the regional development and protection of minority villages in other mountainous areas.

Analysis of influencing factors for complications of anterior thoracolumbar tuberculosis surgery in adults.

This study analyzes the risk factors related to the complications of anterior thoracolumbar tuberculosis in adults and to provide clinical reference. A total of 98 adult patients with thoracolumbar tuberculosis undergoing anterior surgery in our hospital from February 2020 to December 2023 were selected, and the clinical data and postoperative complications were collected. The clinical characteristics were analyzed, and the risk factors related to surgical complications were analyzed by univariate analysis and multi-factor logistic regression model. A total of 54 patients had 75 postoperative complications of different degrees. Univariate analysis showed that patient age, preoperative hemoglobin (Hb) level, bone graft fusion method, intraoperative blood loss, and postoperative neurological function [Asian neurological function classification] were correlated with the occurrence of postoperative complications (P < .05). Multivariate Logistic regression analysis showed that age over 60 years old, preoperative Hb < 100 g/L, blood loss ≥ 800 mL, bone graft fusion method were independent risk factors for anterior postoperative complications of thoracolumbar tuberculosis (P < .05). The independent risk factors for surgical complications of anterior thoracolumbar tuberculosis were age over 60 years old, preoperative Hb < 100 g/L, blood loss ≥ 800 mL, titanium cage by bone graft fusion. Controlling these variable factors before operation can reduce the incidence of postoperative complications.

An Information-Theoretic Asset Pricing Model

Abstract We show that a non-parametric estimate of the pricing kernel, extracted using an information-theoretic approach, delivers smaller out-of-sample pricing errors and a better cross-sectional fit than leading multi-factor models. The information stochastic discount factor (I-SDF) identifies sources of risk not captured by standard factors, generating very large annual alphas (20–37%) and Sharpe ratio (1.1). The I-SDF extracted from a wide cross-section of equity portfolios is highly positively skewed and leptokurtic, and implies that about a third of the observed risk premia represent compensation for 2.5% tail events. The I-SDF offers a powerful benchmark relative to which competing theories and investment strategies can be evaluated.

Prevalence of overweight and obesity among preschool children in Hainan: a cross-sectional study in China's largest free-trade zone.

Childhood obesity leads to significant health risks, emphasizing the critical need for effective preventive measures during the preschool years. However, there is a lack of comprehensive studies on overweight and obesity among preschool children in Hainan Island, China's largest free-trade zone. Our study assessed the prevalence of overweight and obesity among preschool children across Hainan Island using Chinese, World Health Organization (WHO), and International Obesity Task Force (IOTF) criteria. Additionally, the potential factors influencing overweight and obesity among preschool children in Hainan Island were further explored. We conducted a cross-sectional survey of children aged 3-6 years covering 18 cities and counties in China's largest free trade zone. The survey primarily involved anthropometric data collection and questionnaires on children's information and the sociodemographic characteristics of their parents or guardians. We recorded the height and weight of each child and calculated their body mass index (BMI). The z-score for BMI-for-age was calculated using the WHO-recommended Anthro and AnthroPlus software, and diagnoses of overweight and obesity were determined separately according to the Chinese, WHO, and IOTF criteria. The chi-squared test, t-test, or rank-sum test was applied to describe and statistically analyze the baseline characteristics of the participating children. Additionally, potential factors affecting overweight and obesity were analyzed using a multifactor binary logistic regression model. The prevalence of overweight and obesity among preschool children in Hainan Island was 11.30% based on the Chinese criteria, significantly higher than the rates of 5.80% and 5.40% observed according to the WHO and IOTF criteria, respectively (χ 2 = 12,870.368, P < 0.001). After multifactorial adjusted analyses, we found that according to the three growth criteria, having overweight fathers or mothers, family incomes of more than 100,000 Yuan, fully active eating, and higher birth weights increased the risk of overweight and obesity among preschool children in Hainan Island. The prevalence of overweight and obesity among preschool children in Hainan Island was lower compared to rates reported in other regions. Considering that the growth and development indicators for children under 7 years old in Hainan Island are generally lower than international standards, the use of Chinese standards may be more suitable for detecting overweight and obesity among preschool children in this area. Based on the risk factors identified in this study, preventive measures can be targeted in the future to reduce the risk of overweight and obesity in preschool children in Hainan Island.

Numerical analysis of sodium diffusion in aluminum electrolysis cathode carbon blocks based on a microstructure multi-factor corrected model.

Current researches on sodium penetration in electrolytic aluminum cathode carbon blocks primarily measure cathode expansion curves, showing mostly macroscopic characteristics. However, the microscopic structure is often underexplored. As a porous medium, the diffusion performance of cathode carbon blocks is closely tied to their internal pore structure. Viewing the cathode carbon block as a multiphase composite material, this study examines the sodium diffusion process from a microstructural perspective. A prediction model for sodium diffusion, considering factors like porosity, temperature, binding effects, current density, and molecular ratio, was developed. A random aggregate model was implemented in Python and imported into finite element software to simulate sodium diffusion using Fick's second law. Results indicate that increased porosity, higher temperatures, reduced binding effects, increased current density, and higher molecular ratios enhance sodium infiltration, reducing diffusion resistance and increasing the diffusion coefficient. The simulation aligns well with experimental results, confirming its accuracy and reliability.

Maximum Conditional Alpha Test for Conditional Multi-Factor Models

Maximum Conditional Alpha Test for Conditional Multi-Factor Models

Validation of a Questionnaire Assessing Pregnant Women's Perspectives on Addressing the Psychological Challenges of Childbirth.

Pregnant women's experiences and concerns regarding childbirth are complex, necessitating a multidimensional and personalized approach in maternal care. This study explores the psychological and emotional factors influencing pregnant women's decisions regarding their mode of delivery. The results will provide valuable insights for the development of educational and counseling strategies designed to support pregnant women in making informed and conscious decisions about their childbirth. This cross-sectional study aimed to develop and validate a questionnaire exploring the psychological dimensions of childbirth. Factor analysis was employed to assess emotional perceptions, perceived medical risks, and the impact of cesarean section on pregnant women. The questionnaire was distributed online via Google Forms, using social networks like Facebook and Instagram to ensure rapid and broad accessibility. The questionnaire was available for seven months, from January to July 2023. McDonald's ω, Cronbach's α, average inter-item correlation, and total item correlations were calculated to assess the consistency of the questionnaire items in measuring the same construct. The three-factor model emerged as the primary structure based on exploratory and confirmatory factor analyses (EFA and CFA). The first profile, centered on the psychological and emotional benefits of vaginal birth, highlights the importance of the natural birth experience for the mother's psychological well-being. The second profile addresses concerns about medical risks and the need for interventions. The third profile focuses on perceptions and concerns related to the intelligence and adaptability of children born by cesarean section and the effects of anesthesia. Each profile reflects different strategies for seeking control and security amid childbirth uncertainties. These include emphasizing the psychological benefits of vaginal birth, addressing medical risks, and focusing on the impact of interventions on child development. Understanding these variables is essential for providing appropriate counseling and psychosocial support, thereby optimizing the birth experience and promoting the health of both mother and child. The integration of multi-factor and single-factor models in the questionnaire analysis serves complementary purposes, providing distinct yet interrelated insights into the instrument's structure and validity.

A High-Speed Train Traction Motor State Prediction Method Based on MIC and Improved SVR

The traction motor realizes the mutual conversion of electrical energy and mechanical energy during the train traction and braking process and is a key component of high-speed trains. The normal operation of the motor is directly related to the safety of high-speed train operation. Changes in temperature signals can reflect faults in the traction motor. By analyzing the internal and external influencing factors of temperature signals, a multi-factor prediction model for traction motors is established based on the maximal information coefficient and improved support vector regression. In this model, highly relevant features selections are performed based on time-delayed sequences and the maximal information coefficient. Using the adaptive particle swarm algorithm to optimize the improved support vector regression algorithm can enhance its accuracy and efficiency. Furthermore, using the K-nearest neighbor algorithm for error prediction will yield more accurate results. By comparing the RMSE, MBE, MAE, and other evaluation metrics of different algorithms under various working conditions, the results show that the prediction method proposed in this paper performs well across different working conditions. This method demonstrates greater adaptability to varying conditions and is more suitable for applications involving high-speed trains.

ECONOMETRIC ANALYSIS OF THE INFLUENCE OF INVESTMENTS ON ECONOMIC GROWTH IN UZBEKISTAN

Investments are one of the important factors determining the level of economic growth. The article analyzes the impact of centralized, decentralized investments and investments attracted on the basis of foreign credit on ensuring economic growth in our republic, as well as their econometric modeling. The relationship between investments and the level of economic growth was also analyzed by creating a multifactor econometric model.

Carbon Capture and Storage (CCS) Implementation as a Method of Reducing Emissions from Coal Thermal Power Plants in Poland

The Polish economy, and especially the energy sector, is facing an energy transformation. For decades, most electricity in Poland has been generated from hard coal, but in recent years, renewable energy sources have been gaining an increasing share of the market. The aim of the energy transformation is to reduce the carbon footprint in electricity production, which translates into the decarbonization of the economy, including manufactured products. Currently (2024), increasing the share of renewable energy sources raises major challenges in terms of energy storage or other activities and forces cooperation with flexible sources of electricity generation. One of the challenges is to determine what a decarbonized energy mix in Poland could look like in 2050, in which there would be sources (with a smaller share of coal sources in the mix than currently) of electricity generation based on hard coal with CCS technology. In order to do this in an economically efficient manner, there are aspects related to the location of power plants that would remain in operation or repower current generating units. The added value of the study is the simulation approach to the analysis of the problem of assessing the effectiveness of CCS technology implementation together with the transport and storage infrastructure, as well as the multi-aspect scenario analysis, which can determine the limits of CCS technology effectiveness for a given power unit. Positive simulation results (NPV amounted to 147 million Euro) and the knowledge obtained in the scope of the correlated and simultaneous impact of many important cost factors and prices of CO2 emission allowances make this analysis and its results close to reality. Examples of analyses of the effectiveness of CCS system implementations known from the literature are most often limited to determining linear relationships of single explanatory variables with a specific forecasted variable, even if these are multifactor mathematical models.

IFAL-SLAM: an approach to inertial-centered multi-sensor fusion, factor graph optimization, and adaptive Lagrangian method

Abstract In response to the issues of low accuracy, perception degradation, and poor reliability of single-sensor SLAM technologies in complex environments, this study presents a novel IMU-centered multi-sensor fusion SLAM algorithm (IFAL-SLAM) integrating LiDAR, vision, and IMU, based on factor graph elimination optimization (\textbf{I}MU-centered multi-sensor \textbf{F}usion, \textbf{A}daptive \textbf{L}agrangian methods). The proposed system leverages a multi-factor graph model, centering on the IMU, and applies a covariance matrix to fuse visual-inertial and LiDAR-inertial odometries for bias correction, using loop closure factors for global adjustments. To minimize the optimization costs post-fusion, a sliding window mechanism is incorporated, coupled with a QR decomposition elimination method based on Householder transformation to convert the factor graph into a Bayesian network. Finally, an adaptive Lagrangian relaxation method is proposed, employing matrix-form penalty parameters and adaptive strategies to enhance convergence speed and robustness under high rotational dynamics.Experimental results indicate that the proposed algorithm achieves absolute trajectory errors of approximately 0.58 m and 0.24 m in large and small complex scenes, respectively, surpassing classic algorithms in terms of accuracy and reliability.