Analysis Of Influencing Factors Research Articles

Spatiotemporal Evolution Characteristics and Influence Factor Analysis of the Production–Living–Ecological Space in Laiwu, China, from 2001 to 2018

Spatiotemporal Evolution Characteristics and Influence Factor Analysis of the Production–Living–Ecological Space in Laiwu, China, from 2001 to 2018

Statement of Retraction: Analysis of influence factors of marine fishery industry structure optimization on carbon emission efficiency based on depth feedforward model

Statement of Retraction: Analysis of influence factors of marine fishery industry structure optimization on carbon emission efficiency based on depth feedforward model

Adverse event signal mining and serious adverse event influencing factor analysis of fulvestrant based on FAERS database

Fulvestrant, as the first selective estrogen receptor degrader, is widely used in the endocrine treatment of breast cancer. However, in the real world, there is a lack of relevant reports on adverse reaction data mining for fulvestrant. To perform data mining on adverse events (AEs) associated with fulvestrant and explore the risk factors contributing to severe AEs, providing a reference for the rational use of fulvestrant in clinical practice. Retrieved adverse event report information associated with fulvestrant from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database, covering the period from market introduction to September 30, 2023. Suspicious AEs were screened using the reporting odds ratio (ROR) and proportional reporting ratio methods based on disproportionality analysis. Univariate and multivariate logistic regression analyses were conducted on severe AEs to explore the risk factors associated with fulvestrant-induced severe AEs. A total of 6947 reports related to AEs associated with fulvestrant were obtained, including 5924 reports of severe AEs and 1023 reports of non-severe AEs. Using the disproportionality analysis method, a total of 210 valid AEs were identified for fulvestrant, with 45 AEs (21.43%) not listed in the product labeling, involving 11 systems and organs. The AEs associated with fulvestrant were sorted by frequency of occurrence, with neutropenia (325 cases) having the highest number of reports. By signal strength, injection site pruritus showed the strongest signal (ROR = 658.43). The results of the logistic regression analysis showed that concurrent use of medications with extremely high protein binding (≥ 98%) is an independent risk factor for severe AEs associated with fulvestrant. Age served as a protective factor for fulvestrant-related AEs. The co-administration of fulvestrant with CYP3A4 enzyme inhibitors did not show statistically significant correlation with the occurrence of severe AEs. Co-administration of drugs with extremely high protein binding (≥ 98%) may increase the risk of severe adverse reactions of fulvestrant. Meanwhile, age (60–74 years) may reduce the risk of severe AEs of fulvestrant. However, further clinical research is still needed to explore and verify whether there is interaction between fulvestrant and drugs with high protein binding through more clinical studies.

Influencing Factors and Survival Analysis of Late Readmission after Percutaneous Coronary Intervention in Patients with Acute Myocardial Infarction

Objective: This study aimed to explore the influencing factors analysis of late readmission after patients with acute myocardial infarction received percutaneous coronary intervention (PCI). Methods: A total of 368 patients with acute myocardial infarction who received PCI treatment in West China Hospital, Sichuan University/West China School of Nursing, Sichuan University from January 2018, to January 2021, were selected for the study. Among them, 110 subjects were excluded, and 258 subjects were finally included, of which 124 were readmitted and 134 were not readmitted. The baseline data and clinical characteristics of the patients were collected, and the influencing factors of readmission were analyzed by logistic regression analysis. The readmitted patients were followed up for 12 months. The Kaplan–Meier method was used to analyze the survival of patients with delayed readmitted and calculate the survival rate. Results: Significant differences were found between readmitted patients and non-readmitted patients in terms of age, chronic obstructive pulmonary disease (COPD), history of early coronary heart disease, history of hypertension, history of oral anticoagulant drugs, and left ventricle ejection fraction (LVEF, p < 0.05). No significant differences were observed in gender, body mass index, family history of acute myocardial infarction, history of chronic kidney disease, history of diabetes, history of smoking, history of drinking, and the number of implanted stents and diseased vessels (p > 0.05). Binary logistic regression analysis showed that age, COPD, history of premature coronary heart disease, history of oral anticoagulant drugs, and LVEF were important influencing factors of delayed readmission after PCI (all p < 0.05). Follow-up results showed that 125 patients survived and nine died among the delayed non-readmission patients after PCI. Among the patients with delayed readmission, 95 survived and 29 died. Kaplan–Meier survival analysis showed that the survival time of patients with delayed non-readmission was longer than that of patients with delayed readmission, and the difference was statistically significant (χ2 = 17.696, p < 0.001). Conclusion: Age, COPD, history of oral anticoagulant drugs, and LVEF are important influencing factors of delayed readmission after PCI, and the survival time of patients with delayed non-readmission is longer than that of patients with delayed readmission.

Comparative analysis of symptom burdens and influential factors among diverse terminally-ill patients.

The suffering experienced by terminally-ill patients encompasses physiological, psychosocial and spiritual dimensions. While previous studies have investigated symptom burden intensity for specific disease groups, such as cancer or heart failure patients, a research gap exists in understanding major distressing symptoms among diverse terminally-ill patients. This study assessed symptom burden intensity and explored its influential factors among diverse patient disease groups. This cross-sectional study utilized the baseline Integrated Palliative care Outcome Scale (IPOS) assessment data. The study participants were terminally-ill patients enrolled in an end-of-life care (EoLC) intervention in Hong Kong. Statistical methods including relative importance index (RII), one-way analysis of variance (ANOVA), and generalized linear regression (GLR) were employed. Final sample consisted of 1,549 terminally-ill patients (mean age =77.4 years, SD =11.6). The five top-rated distressing symptoms among these patients, revealed by the RII analysis, were poor mobility (RII =64.4%), family anxiety (RII =63.5%), sharing feelings with family/friends (RII =61.4%), weakness/ lack of energy (RII =58.1%), and hardly feeling at peace (RII =50.7%). One-way ANOVA showed significant differences among the eight disease groups in perceived physical and emotional symptom burden intensity (P<0.05). Analysis of RII symptom scores for each disease group revealed that poor mobility was rated as the most distressing symptom (RII =85.1-62.9%) by patients with motor neurone disease, Parkinson's disease, heart failure, dementia, end-stage renal disease and other serious diseases (including stroke, hematological disease, multiple sclerosis and liver diseases). Perceived family anxiety (RII =66.1%) and shortness of breath (RII =63.8%) were the most distressing symptoms for cancer patients and those with chronic obstructive pulmonary disease, respectively. GLR analysis showed that illness type is the most significant factor influencing the perceived burden intensity in terms of the IPOS total and subscale scores of physical symptoms, emotional symptoms and communication/practical issues. Demographic characteristics such as age, gender, marital status and co-residing status were also identified as influential factors of various symptom categories. However, patients' educational level and relationship with primary caregiver did not significantly influence any perceived symptom burden. This study provides valuable insights into the symptom burdens experienced by diverse patient disease groups at end-stage of life. The findings highlight the major distressing symptoms of poor mobility, family anxiety, and shortness of breath. Addressing these symptoms is crucial in improving the quality of care for terminally-ill patients. Furthermore, the study identifies influential factors that can affect the perceived intensity of symptom burden, primarily the main type of terminal illness and patient's age. Tailored care support and improved clinical care should be implemented, particularly for high-risk groups such as patients with non-cancer terminal illnesses and older aged patients. These findings contribute to existing literature and emphasize the need for comprehensive and individualized care in EoLC.

Unveiling the Influential Factors and Heavy Industrial Applications of Graphene Hybrid Polymer Composites

Graphene hybrid-filler polymer composites have emerged as prominent materials that revolutionize heavy industries. This review paper encapsulates an in-depth analysis of different influential factors, such as filler/graphene type, aspect ratios, dispersion methods, filler-matrix compatibility, fiber orientation, synergistic effects, different processing techniques, and post-curing conditions, which affect the processing and properties of graphene hybrid polymer composites, as well as their resultant applications. Additionally, it discusses the substantial role of graphene reinforcement with other fillers, such as carbon nanotubes, silica, nano-clays, and metal oxides, to produce functionalized hybrid polymer composites with synergistically enhanced tailored properties, offering solutions for heavy industries, including aerospace, automotive, electronics, and energy harvesting. This review concludes with some suggestions and an outlook on the future of these composite materials by emphasizing the need for continued research to fully optimize their potential.

Spatial Differentiation and Influencing Factors Analysis of Drought Characteristics Based on the Standardized Precipitation Index: A Case Study of the Yellow River Basin

It is crucial to identify drought characteristics and determine drought severity in response to climate change. Aiming at the increasingly serious drought situation in the Yellow River Basin, this study firstly selected the standardized precipitation index (SPI) and streamflow drought index (SDI) to analyze the characteristics of drought seasons, then identified the frequency, duration, and intensity of drought based on the run theory, and finally recognized the abrupt changing and driving factors of major drought events in specific years by the Mann–Kendall trend test. The conclusions showed the following: (1) The drought in the downstream of the Yellow River Basin was more severe than that in the upstream. The drought characteristics showed significant regional differentiation and deterioration. (2) The drought intensity and duration had an obvious spatial correlation. Compared with the other seasons, the drought duration and severity in spring and autumn were the most serious, and in winter, they showed an aggravating trend. (3) According to a time series analysis of drought conditions in the Yellow River Basin, the worst drought occurred in 1997–2001 with the least rainfall on record and a sudden rise in temperatures. This study could provide a scientific reference for agricultural drought disaster prevention and mitigation.

An innovative data-feature-driven approach for CO2 emission predictive analytics: A perspective from seasonality and nonlinearity characteristics

This study enhances data support for CO2 emission reduction strategies across the U.S., targeting the commercial, industrial, residential, and electric power sectors. Despite the availability of numerous predictive models for sector-specific CO2 emission forecasts, a gap remains for a versatile model adept at managing time series data with varied characteristics. We introduce a novel, highly generalizable forecasting model that merges Conformable Fractional Accumulation, seasonal dummy variables, and the time power item to address randomness, seasonality, and nonlinearity in predictions. Our model outshines prevailing competitors, including statistical (AR and ARIMA), machine learning (LSTM, BPNN, and GRU), and grey models (SGM (1,1) and GM (1,1)), in comparative tests. It achieves MAPE values below 5% and 10% for training and testing phases, respectively, across all sectors, surpassing the inconsistent results from benchmark models. Furthermore, the stability and reliability of our model are validated through a systematic robustness verification framework. Notably, leveraging this model's superior predictive performance, we forecast future sectoral CO2 emissions. A detailed analysis of sector-specific influencing factors provides actionable insights for crafting targeted decarbonization policies and plans.

Establishment, performance evaluation, and influence factors analysis of the α-amylase reference measurement procedure

Establishment, performance evaluation, and influence factors analysis of the α-amylase reference measurement procedure

Application of data mining technology in analysis of influencing factors of Smart electric power emergency rescue

Because electric emergency rescue is affected by many factors, there are certain problems in the rescue process. In order to improve the power emergency rescue effect, based on data mining technology, this paper selects the neural network algorithm as the main analysis algorithm of the model, and uses the multi-layer forward neural network topology to perform modeling and correlation analysis. Moreover, this paper establishes the functional architecture of the power system emergency plan deduction system based on the analysis of the demand for the power system emergency plan deduction system. In addition, this paper combines the power emergency rescue system requirements to build the model function module, proposes a mobile model based on the emergency rescue scene, and analyzes its realization process. Finally, in order to study the system performance, this paper designs experiments to analyze the model performance, and combines mathematical statistics to draw charts and score the system model. The research results show that the power rescue influence factor analysis model constructed in this paper is effective.

Factors associated with the level of self-management in elderly patients with chronic diseases: a pathway analysis

BackgroundTo analyze the effects and pathways of factors such as psychological capital, family functioning, and sources of meaning in life on the level of self-management in elderly patients with chronic diseases and to provide a basis for the development of relevant nursing interventions in the future.MethodsConvenience sampling was used to select elderly patients with chronic diseases who underwent medical checkups and consultations at three community hospitals in Jinzhou city from March 2023 to October 2023, and the self-designed General Information Questionnaire (GIS), Psychological Capital of the Elderly Scale (PCE), Family Functioning Index Questionnaire (APGAR), Sources of Meaning of Life Scale for Older Adults(SMSE), and Self-Management Behavior of Chronic Patients Scale (SMCS) were used. SPSS 26.0 was used for data entry, one-way analysis, Pearson correlation analysis, and multiple linear regression were used to analyze the data, and Amos 17.0 was used to construct the structural equation model.ResultsA total of 355 elderly patients with chronic diseases were included, and their self-management score was 74.75 ± 12.93, which was moderate. The results of the influencing factor analysis showed that the influencing factors of the self-management level of elderly chronic disease patients were age, years of illness, psychological capital, family functioning, and sources of meaning in life (p < 0.05). Path analysis revealed that sources of meaning in life were a partial mediator of the relationship between psychological capital and self-management, with an effect value of 0.166 (95% CI: 0.042,0.391), accounting for 37.6% of the total effect; life meaning was a partial mediator of family functioning and self-management level, with an effect value of 0.231 (95% CI: 0.040,0.452), accounting for 54.0% of the total effect. accounting for 54.0% of the total effect.ConclusionThe self-management of elderly patients with chronic diseases is intermediate. Healthcare professionals should actively implement holistic healthcare management measures from the family aspect to help patients understand the meaning of life and improve the level of patients’ psychological capital to improve the self-management level of elderly patients with chronic diseases.

Algorithm for community security risk assessment and influencing factors analysis by back propagation neural network

This paper aims to accurately assess and effectively manage various security risks in the community and overcome the challenges faced by traditional models in handling large amounts of features and high-dimensional data. Hence, this paper utilizes the back propagation neural network (BPNN) to optimize the security risk assessment model. A key challenge of researching community security risk assessment lies in accurately identifying and predicting a range of potential security threats. These threats may encompass natural disasters, public health crises, accidents, and social security issues. The intricate interplay of these risk factors, combined with the dynamic nature of community environments, presents difficulties for traditional risk assessment methodologies to address effectively. Initially, this paper delves into the factors influencing safety incidents within communities and establishes a comprehensive system of safety risk assessment indicators. Leveraging the adaptable and generalizable nature of the BPNN model, the paper proceeds to optimize the BPNN model, enhancing the security risk assessment model through this optimization. Subsequent comparison experiments with traditional models validate the rationality and effectiveness of the proposed model, with hidden layer nodes set at various levels like 10, 15, 20, 25, 30, and 35. These traditional models include Convolutional Neural Network (CNN), Long Short-Term Memory Network (LSTM), Bidirectional Encoder Representations from Transformers (BERT), Generative Pre-trained Transformer (GPT), and eXtreme Gradient Boosting (XGBOOST). Experimental findings demonstrate that with 20 hidden layer nodes, the optimized model achieves a remarkable final recognition accuracy of 99.1 %. Moreover, the optimized model exhibits significantly lower final function loss compared to models with different node numbers. Increasing the number of hidden layer nodes may diminish the optimized model's fit and accuracy. Comparison with traditional models reveals that the average accuracy of the optimized model in community risk identification reaches 98.5 %, with a maximum accuracy of 99.6 %. This marks an improvement of 9%–11 % in recognition accuracy across various risk factors compared to traditional models. Regarding system response time and resource utilization, the optimized model exhibits a response time ranging from 100 ms to 120 ms and consistently lower resource utilization rates across all scenarios, underscoring its efficiency in community security risk assessment. In conclusion, this experiment sheds light on the underlying mechanisms and patterns of community safety risk formation, offering novel perspectives and methodologies for researching community safety risk assessment. The paper concludes by presenting recommendations and strategies for addressing community safety risks based on experimental analysis.

Navigating Post-COVID-19 Social–Spatial Inequity: Unravelling the Nexus between Community Conditions, Social Perception, and Spatial Differentiation

The 2023 SDGs report underscores the prolonged disruption of COVID-19 on community living spaces, infrastructure, education, and income equality, exacerbating social and spatial inequality. Against the backdrop of the dual impact of significant events and the emergence of digital technologies, a coherent research trajectory is essential for characterizing social–spatial equity and understanding its influential factors within the urban planning discipline. While prior research emphasized spatial dimensions and mitigated spatial differentiation to ensure urban equity, the complexity of these interconnections necessitates a more comprehensive approach. This study adopts a holistic perspective, focusing on the “social–spatial” dynamics, utilizing social perception (sentiment maps) and spatial differentiation (housing prices index) pre- and post-pandemic to elucidate the interconnected and interactive nature of uneven development at the urban scale. It employs a multi-dimensional methodological framework integrating morphology analysis of housing conditions, GIS analysis of urban amenities, sentiment semantic analysis of public opinion, and multiscale geographically weighted regression (MGWR) analysis of correlation influential factors. Using Suzhou, China, as a pilot study, this research demonstrates how these integrated methods complement each other, exploring how community conditions and resource distribution collectively bolster resilience, thereby maintaining social–spatial equity amidst pandemic disruptions. The findings reveal that uneven resource distribution exacerbates post-pandemic social stratification and spatial differentiation. The proximity of well-maintained ecological environments, such as parks or scenic landmarks, generally exhibits consistency and positive effects on “social–spatial” measurement. Simultaneously, various spatial elements influencing housing prices and social perception show geographic heterogeneity, particularly in areas farther from the central regions of Xiangcheng and Wujiang districts. This study uncovers a bilateral mechanism between social perception and spatial differentiation, aiming to delve into the interdependent relationship between social–spatial equity and built environmental factors. Furthermore, it aspires to provide meaningful references and recommendations for urban planning and regeneration policy formulation in the digital era to sustain social–spatial equity.

Lamb waves-based PCF-DMA: An anti-interference synchronous independent data transmission scheme for multiple cross-space users

Due to the high cost and safety risk brought about by wire penetration within the aerospace and underwater structure, reliable wireless cross-space multiple access transmission techniques become highly necessary. Lamb waves data transmission is recognized as a feasible solution, since it utilizes the solid structures as the transmission medium without being affected by electromagnetic radiation. However, in the case of synchronous transmission of multiple users, the aggravation of inter-access interference would severely weaken the reliability of the transmission system. Focusing on the issue of anti-interference synchronous independent data transmission for multiple cross-space users, this study proposes a Lamb waves-based pulse code frequency division multiple access (PCF-DMA) scheme. Firstly, a fast pulse position modulation strategy is established to improve the transmission rate. Considering the characteristics of the Lamb waves excitation pulses as the codeword, a PCF-DMA multiple access encoding method is designed. Secondly, by sparsely representing the Lamb waves physical transmission channel, a shift-invariant sparse deconvolution technology is proposed for decoding. Specially, to achieve reliable multiple access transmission with the PCF-DMA scheme, a normalized cross-correlation coefficient is used as the design criterion of the Lamb wave pulses. To validate these concepts, a three-user PCF-DMA data transmission system based on Lamb waves is built. The experimental results illustrate that the proposed design criterion can accurately predict the reliability of the multiple access transmission system. The bit error rate (BER) of the proposed PCF-DMA scheme is 30 % lower than that of conventional FDMA within a signal-to-noise ratio of −12 to 8 dB. Further, the influence factor analysis for the BER and communication rate are conducted. Finally, a synchronous independent data transmission for multiple cross-space users is achieved, with a maximum communication rate of 92.9 kbps per user and a BER below 0.17 %.

Measurement and influencing factor analysis of the matching degree between agricultural scientific and technology innovation and rural industry integration under the sustainable background

Agricultural scientific and technological innovation and rural industry integration are the key contents of promoting agricultural and rural modernization in China. This study empirically analyzed the matching relationship between the two and its influencing factors, and found that the matching degree between agricultural scientific and technological innovation and rural industry integration in China showed a steady upward trend in the sample period. Both China as a whole and all regions completed the upgrading from moderate to high matching in the sample period, The performance of each region is also excellent. In 2020, the matching degree of 28 regions will enter a high matching range. The implementation of the rural revitalization policy will help to improve the matching degree between China’s agricultural scientific and technological innovation and rural industrial integration. The scale of rural financial development and the matching degree of lagging one period also have a significant positive impact on the improvement of the matching degree. This study further proposes relevant policy recommendations from improving the supply level of agricultural scientific and technology innovation, enhancing the adaptability between supply and demand of agricultural scientific and technology innovation, improving the policy support system, and optimizing the financial security system.

Change trend of birth rate of our country population and influence factor analysis

On the ground of time series analyses, this paper excavates the trend in preliminary and carries out some short-term forecasts about future birthrate variation in wish to shed light on its solutions. This paper attempts to excavate the influence that several dominant factors exert on birthrates via regression analyses. The cluster analyses are utilized to filter out representative provinces as samples for subsequent research in order to tackle the problem that the correlations between dependant variable and each independent variable is not significant. Wielding SPSS as an analytical tool, we construct the regression model about how price level in combination with education level influences the birthrates.

Intelligence level evaluation and influencing factors analysis of equipment manufacturing industry in the Yangtze River Delta.

The Yangtze River Delta (YRD) bears the vital task of driving the growth of China's equipment manufacturing industry (EMI) intelligence as an advanced region. Fostering the transformation and upgrading of the EMI in the YRD and constructing a modern production mode is vital to developing and reforming China's manufacturing industry. This paper uses industrial robot data to assess the level of intelligence (LoI) in the EMI from 2016 to 2019. The OLS (ordinary least squares) model is used for the measurements, and the MQ (the modified contribution index) is used to estimate the degree of contribution from a host of variables. It is identified that the LoI is on the rise. However, excluding railways, aerospace, shipbuilding, and other transportation equipment manufacturing, the LoI is significantly higher than in other subsectors. It is also identified that technological innovation ability, human capital density, and enterprise cost pressure govern the industry's LoI. Moreover, while there is a difference in the main influencing factors in LoI within different industries, R&D investment, technological innovation ability, and enterprise cost pressure have the most significant impact across most equipment manufacturing sub-industries.

Pollution load estimation and influencing factor analysis in the Tuhai River Basin in Shandong Province of China based on improved output coefficient method.

Estimating the pollution loads in the Tuhai River is essential for developing a water quality standard scheme. This study utilized the improved output coefficient method to estimate the total pollution loads in the river basin while analyzing the influencing factors based on the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model. Findings indicated that the projected point source pollution loads for total phosphorus (TP), chemical oxygen demand (COD), and ammonia nitrogen (AN) would amount to 3937.22 ton, 335,523.25 ton, and 13,946.92 ton in 2021, respectively. Among these, COD pollution would pose the greatest concern. The primary contributors to the pollution loads were rural scattered life, large-scale livestock and poultry breeding, and surface runoff. Per capita GDP emerged as the most influential factor affecting the pollution loads, followed by cultivated land area, while the urbanization rate demonstrated the least impact.

The Analysis of Influence Factors and Solutions to the Lack of Sex Education for Minors in China from the Perspective of the Ecological Systems Theory

Nowadays the implementation of sex education is still insufficient in China, although it has been improved gradually. This paper looks at the dilemma in sex education in modern society in China, which is mainly concerned with the tension between the lack of systematic education on sex at all ages and the need for teenagers to better protect themselves from danger. Actually, sex education is of great significance to help children cultivate a sense of identity and gender equality, consciously respect members of the opposite sex, establish a sense of self-protection, and confront the concept of sex correctly. However, as a result of cultural conservatism, few educational systems in China have a clear sense of sex in all aspects. The study reveals the influence factors on the lack of sex education for minors in China from the perspective of the ecological systems theory, pointing out the negative impact of its absence and potential solutions to relieve the tension. Influence factors include the historical legacy Impact of Cultural Conservatism and the lack of policy support for the sexual education of minors, which belong to the ecosystems, while the impact of social media belongs to the macro system.

Experimental and numerical study on seismic behavior of precast shear walls assembled with a socket vertical joint

In the development of precast shear wall structures, reliable and efficient connections of precast units are crucial for their seismic behavior. Meanwhile, the heavy precast units using conventional concrete bring a low lifting efficiency. Considering these issues, the paper proposed a novel type of socket vertical joint for precast shear walls made of steel fiber reinforced lightweight aggregate concrete (SFRLAC). Sixteen socket connection specimens, with four socket depths (100 mm, 150 mm, 200 mm, and 250 mm) and two interface treatments (smooth and exposed aggregate), underwent interface bonding performance tests using uniaxial tensile testing and finite element (FE) numerical models to determine the appropriate socket depth. Three specimens were tested using the low-cyclic loading test, including two reinforced SFRLAC precast shear walls assembled with a socket vertical joint and a cast-in-place reinforced SFRLAC shear wall as a reference. The failure patterns and process of the shear walls were observed, while the load-displacement curves were obtained. Additionally, a parametric analysis of different influencing factors was conducted using finite element (FE) numerical models. This analysis studied the bearing capacity, energy dissipation capability, deformation ability, and stiffness degradation of the shear walls. Results indicate that when the precast shear walls were damaged, the socket vertical joint was integrity and failed in bending-shear. The three specimens exhibited fewer differences in seismic behavior with relatively full hysteretic curves. The seismic mechanisms of the shear wall assembled with a socket vertical joint were elucidated by analyzing the numerical distribution of stress in both SFRLAC and steel bars at different stress levels. Furthermore, an expansion analysis was conducted to assess the impact of axial load ratios and shear to span ratios on the seismic behavior of precast shear walls.