Analysis Of Influencing Factors Research Articles

Study on the measurement method and influencing factor analysis for the torsional angle of crankshaft

An improved method is proposed to obtain the torsional angle at both ends of the crankshaft simply and reliably. The installation position deviation is removed with a novel correction method. A digital filter is used to decrease the fluctuation of zero crossing point by 69.8%. The relative variation of torsional angle is increased from 0.818 ℃A and 0.347 ℃A to 1.075 ℃A and 0.474 ℃A respectively for the first and fifth cylinder when the torque is increased, which is attributed to the increasing combustion pressure. With the increasing engine speed, the relative variation of torsional angle increases due to the increasing inertia force. The relative variation of torsional angle is not noticeable under different oil temperature due to the comprehensive influence of combustion and friction torque. The amplitudes of most orders have similar changing trend with the relative variation of torsional angle.

Analysis of Influence Factors on Acceptance of the Access by KAI System using Technology Acceptance Model Framework

Analysis of Influence Factors on Acceptance of the Access by KAI System using Technology Acceptance Model Framework

Ecological risk assessment and influencing factor analysis of the Yellow River basin based on LUCC and boosted regression tree

Ecological risk assessment and influencing factor analysis of the Yellow River basin based on LUCC and boosted regression tree

Analysis of influencing factors of traffic accidents on urban ring road based on the SVM model optimized by Bayesian method.

Based on small scale sample of accident data from specific scenarios, fully exploring the potential influencing factors of the severity of traffic accidents has become a key and effective research method. In order to analyze the factors mentioned above in the scenario of urban ring roads, this paper collected data records of 1250 traffic accidents involving different severity on urban ring road of a central city in northwest China in the past 3 years. Firstly, the Support Vector Machine (SVM) model of non-parametric method is utilized to analyze the data above, and three kernel functions of linear, inhomogeneous polynomial and Gaussian radial basis are constructed respectively. Considering comprehensively 16 potential influencing factors covering the driver-vehicle-road-environment integrated system, the SVM models of above three kernel functions are verified, accuracy reaches 0.771 and F1 reaches 0.841. Then, Bayesian Optimization (BO), Grids Search (GS) and Rough Set (RS) are utilized as optimizer to adjust the parameters of Gaussian radial basis function SVM model, the performance of BO-SVM is further improved and reaches the optimum, with an average accuracy of 0.875 on the test set and a F1 of 0.886, completely outperforming the benchmark models of GS-SVM, RS-SVM, Bilayer-LSTM and BP. Finally, the sensitivity analysis method is utilized to quantify the sensitivity of the potential influencing factors to the severity of road accidents, and the backward selection method is utilized to screen the core influencing factors that influence the severity of accident, concluded that core influencing factors are age, driving mileage and vehicle type. This paper will provide reference for the analysis of the significant influencing factors for road accidents severity, and to provide theoretical support for the precise formulation of accident improvement strategies.

Spatiotemporal Variation and Influences of Acidification in the North Pacific, 1995–2019

The continuous rise in atmospheric CO2 levels has led to persistent ocean acidification, which negatively impacts marine environments crucial for marine life and alters the chemical composition of seawater. This phenomenon carries significant implications for human society. Utilizing surface seawater pH data from the North Pacific spanning 1995 to 2019, this study investigates the overall and localized spatiotemporal variations in pH within the region, as well as the factors influencing these variations. Additionally, it conducts a quantitative analysis of the different influencing factors. The findings reveal a consistent downward trend in surface seawater pH in the North Pacific, decreasing from 8.073 to 8.029, with notable seasonal variations. The highest pH values are recorded in winter, followed by spring, with lower values in autumn and summer. Spatially, the pH values are higher in the northwest and lower in the southeast, with the most pronounced acidification occurring in the central and western regions, while other areas exhibit more uniform acidification levels. Spatial correlation analysis indicates that surface seawater pH in the North Pacific generally shows a negative correlation with sea surface temperature (SST), salinity (SSS), and chlorophyll-a concentration (chl a) and a positive correlation with dissolved oxygen (DO). Among these factors, SST exerts the greatest influence on seawater pH, followed by DO and SSS. The degree of acidification varies across different regions, and the dominant influencing factors differ accordingly. In the equatorial central region (A), the primary factors are chl a and SST; in the eastern regions of China and Japan (B) and the western region of Canada (C), DO and SSS are the main controlling factors. An interaction analysis of each pair of dominant factors using the geodetector shows that their respective contributions to regions A, B, and C are 70%, 90%, and 50%, respectively. Understanding the primary factors driving acidification in different regions can aid in comprehending the biological and environmental impacts of acidification in those areas and provide valuable insights for mitigating marine acidification.

Adverse event signal mining and severe adverse event influencing factor analysis of Lumateperone based on FAERS database.

Lumateperone has been approved by the Food and Drug Administration (FDA) for the treatment of schizophrenia in adults since 2019, however, there is still a lack of data report on adverse reactions in real-world settings. Conducting data mining on adverse events (AEs) associated with Lumateperone and investigating the risk factors for serious AEs can provide valuable insights for its clinical practice. AE reports in the FDA Adverse Event Reporting System (FAERS) from 2019 Q4 (FDA approval of Lumateperone) to 2024 Q1 were collected and analyzed. Disproportionality in Lumateperone-associated AEs was evaluated using the following parameters: Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS). Univariate and multivariate logistic regression analyses were conducted to identify the risk factors for Lumateperone-induced severe AEs. A total of 2,644 reports defined Lumateperone as the primary suspected drug was collected, including 739 reports classified as severe AEs and 1905 reports as non-severe AEs. The analysis revealed that 130 preferred terms (PTs) with significant disproportionality were based on the four algorithms, 67 (51.53%) of which were not included in the product labeling, affecting 6 systems and organs. In addition, dizziness (81 cases) was the most reported Lumateperone-associated severe AEs, and tardive dyskinesia showed the strongest signal (ROR = 186.24). Logistic regression analysis indicated that gender, bipolar II disorder, and concomitant drug use are independent risk factors for Lumateperone-associated severe AEs. Specifically, female patients had a 1.811-fold increased risk compared with male patients (OR = 1.811 [1.302, 2.519], p = 0.000), while patients with bipolar II disorder had a 1.695-fold increased risk compared with patients diagnosed with bipolar disorder (OR = 1.695 [1.320, 2.178], p = 0.000). Conversely, concomitant use of CYP3A4 inhibitors or drugs metabolized by CYP3A4 was associated with a decreased risk of severe AEs (OR = 0.524 [0.434, 0.633], P = 0.000). Collectively, this study provides critical insights into the safety profile of Lumateperone. It highlights the need for cautious use in high-risk populations, such as females and individuals with bipolar II disorder, and emphasizes the importance of monitoring for AEs, including dizziness and tardive dyskinesia. Healthcare also should remain alert to potential AEs not listed in the prescribing information to ensure medical safety.

Prediction of the internal corrosion rate for oil and gas pipelines and influence factor analysis with interpretable ensemble learning

Corrosion is one of the major threats to the safety and reliability of oil and gas pipelines, making accurate prediction of corrosion rate crucial for pipeline maintenance and repairment. Traditional prediction methods often ignore more critical factors and lack interpretability, which hinders the practical application. Here, an interpretable ensemble machine learning framework is proposed, not only improving prediction performance, but also enhancing interpretability for predicting the internal corrosion rate of oil and gas pipelines. In this work, ExtraTreeRegression model has demonstrated superior prediction accuracy relative to the other five machine learning models, and the determination coefficient of the ExtraTreeRegression model achieves 0.93 after feature engineering. Then, Shapley Additive exPlanations (SHAP) values is utilized to visually interpret the model locally and globally to help account for the contributions of the input features. Furthermore, the accumulated local effect (ALE) successfully explains how the features affect the internal corrosion rate of oil and gas pipelines. By collecting corrosion data of oil and gas pipeline and performing feature engineering and data preprocessing, we construct a comprehensive and reliable prediction model with interpretability. Experimental results demonstrate that the proposed interpretable ensemble machine learning approach outperforms other models in both accuracy and interpretability, providing valuable insights for pipeline management decisions.

Updated Static Influential Factor Analysis for Unidirectional Carbon-Based Composites.

The orientation of carbon fibers significantly affects the dynamic properties of unidirectional carbon-based composites (UCBCs), with variations under different static loads. A previous study analyzed changes in the modal parameters of UCBC structures by using the static load influential factor (SLIF). This study introduces a revised SLIF, derived from a simplified formulation that accounts for shifts in resonance frequency and the in-phase relationship between static load and modal response. The revised SLIF is theoretically linked to the modal participation factor in UCBC structures. The dynamic behavior of UCBCs was studied across six modes-four bending and two torsional-using specimens with five carbon fiber orientations, from 0 to 90 degrees. The revised SLIF showed significant effects in two robust specimens, #1 and #2, and an isotropic SUS304 specimen subjected to uniaxial pre-static load, with resonance frequency variations under 0.16%. In contrast, the original SLIF gave negligible results in the fifth mode due to a damping term, which, when multiplied by the resonance frequency, led to an undetectable indicator. Therefore, the revised SLIF more effectively captures the static load's impact on UCBC dynamic behavior compared with the original method.

Economic assessment of acetaldehyde production from oxidative dehydrogenation of bioethanol through chemical looping air separation

The conventional bioethanol oxidative dehydrogenation to acetaldehyde suffers from low energy efficiency and high capital cost due to cryogenic air separation’s high energy consumption and high capital investment. A novel bioethanol oxidative dehydrogenation to acetaldehyde system through chemical looping air separation is established and analyzed in this paper. According to the prediction results, energy efficiency as well as techno-economic performance for the system are studied. It demonstrates that the whole energy efficiency of the system is 55.91 %. Moreover, the acetaldehyde production cost is $877/tonne with bioethanol price $0.43/L. For the acetaldehyde production scale of 106 tonnes/year, the payback period is 2.40 years. Compared with other acetaldehyde production technologies, the system is competitive in terms of acetaldehyde production cost. Additionally, sensitivity analysis for influence factors is carried out. The price of bioethanol has the greatest impact on acetaldehyde production cost, while the price of water is the minimum influence factor.

Assessing terrestrial water storage dynamics and multiple factors driving forces in China from 2005 to 2020

In the context of global warming, comprehending the dynamics of terrestrial water storage (TWS) and its responses to natural and anthropogenic factors is paramount for hydrological research and the management of water resources in China. This study utilized GRACE (Gravity Recovery and Climate Experiment)/GRACE-Follow On (GRACE-FO) satellite data to analyze terrestrial water storage across nine basins in China from 2005 to 2020 at multiple temporal and spatial scales. Subsequently, employing a Geographic detector model, potential influencing factors were identified, and an enhanced Geographically Weighted Regression (GWR) method was proposed for attributing changes in TWS in China. The findings reveal a consistent declining trend in TWS based on GRACE/GRACE-FO data across different temporal scales, with the most pronounced decreases observed in August and September. Geographic Detector analysis unveils significant interactions among various environmental factors, with climate variables playing a pivotal role in modulating hydrological characteristics of major river basins, where rising temperatures can exacerbate the severity of precipitation events, thus increasing the risk of floods and droughts. Moreover, analysis of the primary influencing factors indicates significant impacts of population density and topography on water resources in the southeastern and southwestern regions, particularly amidst increasing human activities and urbanization expansion. The results of this study are crucial for comprehending the dynamic changes and mechanisms of TWS in China, as well as for formulating water resource management strategies.

Source and Influencing Factor Analysis of Soil Heavy Metals Based on PMF Model and GeoDetector

In Lijiang City, as a typical example, 93 soil samples were collected from the study area, and soil pH； organic matter； and heavy metals arsenic （As）, mercury （Hg）, copper （Cu）, zinc （Zn）, lead （Pb）, cadmium （Cd）, and chromium （Cr） were determined. We explored the sources of heavy metals in the study area by means of Positive Definite Matrix Factorization （PMF） modeling and analyzed the impact of influencing factors by combining seven heavy metals with 13 influencing factors in a GeoDetector. The results showed that the mean values of soil heavy metals ω（As）, ω（Hg）, ω（Cu）, ω（Zn）, ω（Pb）, ω（Cd）, and ω（Cr） in the study area were 17.55, 0.19, 86.75, 164.84, 28.95, 0.39, and 167.87 mg·kg-1, respectively, which were greater than the background values of soils in Yunnan Province （except for As and Pb）. Regarding spatial distribution, the high values of Cu and Cr content were mainly concentrated in Yulong Naxi Autonomous County； the high value areas of As, Hg, Pb, and Cd were mainly concentrated in Ninglang Yi Autonomous County； and the high value of Zn content was mainly concentrated in Huaping County. Correlation analysis and PMF modeling revealed that the main sources of heavy metals As and Hg in the study area were industrial sources, Zn was from transportation pollution sources, Cr and Cu were from natural sources, and Cd and Pb were from agricultural sources. Further, the factor detector of the GeoDetector found that soil pH and organic matter （OC） had strong explanatory power for the content of seven heavy metals, and the interaction detector found that the results following the interaction of different influencing factors were nonlinear enhancement or two-factor enhancement, in which the interaction of OC and pH was the dominant factor for the spatial differentiation of heavy metals. This provides an important scientific basis for the protection of the soil environmental health and sustainable development in Lijiang City.

Spatial correlation network and influencing factors analysis of supply efficiency of basic public service (SEBPS) in rust belt regions of China: An empirical study from Northeast China

Equalizing basic public service is crucial for achieving social fairness and justice. Promoting the improvement of public service can be achieved through the improvement of supply efficiency and cross-regional coordinated development. We employed the Super-SBM model, modified gravity model, and social network analysis method to empirically study the spatial correlation network characteristics of supply efficiency of basic public service (SEBPS) based on the data of 34 prefecture-level cities in the three provinces of Northeastern China from 2011 to 2020. The results revealed the overall efficiency of SEBPS in the study area is relatively low, and the supply efficiency is polarized. There is significant non-equilibrium between regions, and the regional difference moment gradually expands over time. There exists a strong spatial correlation between SEBPS in each city, and the overall correlation effect is weak but the network stability has improved. Each city had different roles and statuses in the spatial correlation network, creating a hierarchical structure. The level of geospatial proximity had a positive impact on the spatial correlation and spillover of the network. The level of economic development and infrastructure configuration hurt the network, and there is obvious heterogeneity between cities. The differences in industrial structure, opening up level, urbanization level, population density, the level of fiscal decentralization and technological innovation and personal income level among cities affected the SEBPS network. This study not only provides a new network research perspective and theoretical foundation for public service policies in China's rust belt region but also serves as a reference for urban development in similar regions.

The response of daytime nitrate formation to source emissions reduction based on chemical kinetic and thermodynamic model

Particulate nitrate is an important component of particulate matter and poses a significant threat to the ecosystem and human health. The gas-phase formation pathway of nitrate is extremely important, which mainly comprises the NO2 oxidation process triggered by OH radicals and the nitrate partitioning process. The response of nitrate to source emission reduction during different pollution periods remains unclear. Here, we applied the chemical kinetic and thermodynamics model to explore the importance oxidation process and partitioning process during different pollution periods based on high-time resolution observation data. The result indicated that with the aggravation of pollution, the partitioning process gradually ceases to be a limiting step in the formation of nitrates. The results of the influencing factor analysis indicate that NO2 concentration and aerosol pH values play a more significant role in the formation of nitrates. Specifically, during the clean period, nitrate formation is sensitive to both NO2 concentration and pH values, but during the pollution period, it becomes sensitive only to NO2 concentration. By combining source apportionment, we explored the response of nitrate formation to source emission reduction, and the results showed that the control of vehicle exhaust emissions and coal combustion sources is more effective in mitigating nitrate pollution. Additionally, this study also emphasized the importance of early prevention and control of pollution sources. This research provides scientific evidence for the precise management and control of nitrates.

The Analysis of Influential Factors on the Delay of Dredging Work at Rebaq Rinding River Line in Kutai Kertanegara Regency, Indonesia

Along with fast progress of industrial world, particularly in the building construction project which also increasing rapidly, the difficulty level in managing and implementing construction projects also rising where the higher the difficulty the longer the time required to complete the project. A delay in work implementation is evident from the work package of Dredging Work Project for Rebaq Rinding River at Muara Muntai District of Kutai Kertanegara Regency. Thus, this research was performed to determine factors that influence project delays. The selected data analysis method was a factor analysis and multiple linear regression analysis from the questionnaire answers that distributed to 45 respondents ranging from the service users, contractors, and consultants involved in the Dredging Work Project in Rebaq Rinding River at Muara Muntai District of Kutai Kertanegara Regency which experienced delays. According to the result of this research, factor that has significant influence to project delays is factor of Work Implementation Method with value of tcount = 3.162 > from ttable = 2.028, and based on T test, it was found that the most dominant factor is the factor of Work Implementation Method with ß value = 0.646. The strategy used in dealing with project delays are: prior to the beginning of work implementation, implementation schedule must be prepared in accordance with the rational work stages and the work stages implementation must follow the planned method.

Analysis of risk influential factors of marine pilots during the embarkation and disembarkation

Due to the complex geographical conditions within the port waters, it is necessary to take pilotage operations. The embarkation and disembarkation (E&D) of marine pilots is the riskiest phase of pilotage work, with accidents occurring frequently. In order to examine the key Risk Influential Factors (RIFs) of E&D accident of marine pilots, in this study, a novel complex network model of RIFs of marine pilots during the E&D process is proposed. Firstly, based on the E&D accident investigation report of marine pilots, the causal chain of E&D accidents is extracted, and the RIFs leading to E&D accidents are identified. Secondly, a complex network model of RIFs of marine pilots during the E&D is constructed based on the complex networks theory, and the network reliability is verified. Finally, by comprehensive use of topology characteristics analysis, robustness analysis and module analysis, the key RIFs of pilot E&D accidents are identified. The research results show that pilot and management factors have important impact on pilot safety, ‘insufficient pilot ladder strength' is the most critical RIF. It should be focused on RIFs with high connectivity between modules and within modules, and the risk evolution can be blocked through the control of those RIFs. Abbreviations: BNs: Bayesian Networks; CDM: Critical Decision Method; DEMATEL: Decision-making Trial and Evaluation Laboratory; E&D: Embarkation and Disembarkation; FSA: Formal Safety Assessment; RIFs: Risk Influential Factors; RPD: Recognition-Primed Decision; SHERPA: Systematic Human Error Reduction and Prediction Approach; SOLAS: International Convention for the Safety of Life at Sea; VTS: Vessel Traffic Services; TECHR: Technique for Early Consideration of Human Reliability.

Analysis of Taxi Demand and Traffic Influencing Factors in Urban Core Area Based on Data Field Theory and GWR Model: A Case Study of Beijing

Urban transportation constitutes a complex and dynamic system influenced by various factors, including population density, infrastructure, economic activities, and individual travel behavior. Taxis, as a widespread mode of transportation in many cities, play a crucial role in meeting the transportation needs of urban residents. By using data field theory and the Geographically Weighted Regression (GWR) modeling method, this study explored the complex relationship between taxi demand and traffic-related factors in urban core areas and revealed the potential factors affecting taxi starting and landing points. This research reveals that during the morning peak hours (7:00–9:00), at locations such as long-distance bus terminals, bus stations, parking areas, train stations, and bike-sharing points, taxi demand significantly increases, particularly in the central and southeastern regions of the urban core. Conversely, demand is lower in high-density intersection areas. Additionally, proximity to train stations is positively correlated with higher taxi demand, likely related to the needs of long-distance travelers. During the evening peak hours (17:00–19:00), the taxi demand pattern resembles that of the morning peak, with long-distance bus terminals, bus stations, and parking and bike sharing areas remaining key areas of demand. Notably, parking areas frequently serve as pick-up points for passengers during this time, possibly associated with evening activities and entertainment. Moreover, taxi demand remains high around train stations. In summary, this study enhances our understanding of the dynamics of urban taxi demand and its relationship with various transportation-related influencing factors within the core urban areas. The proposed grid partitioning and GWR modeling methods provide valuable insights for urban transportation planners, taxi service providers, and policymakers, facilitating service optimization and improved urban mobility.

Mapping Benggang Erosion Susceptibility: An Analysis of Environmental Influencing Factors Based on the Maxent Model

Benggang erosion is one of the most severe geomorphological hazards occurring on deeply weathered crusts in the hilly regions of southern China. Unraveling the susceptibility and pinpointing the risk areas of Benggang erosion are essential for developing effective prevention and management strategies. This study introduced the Maxent model to investigate Benggang erosion susceptibility (BES) and compared the evaluation results with the widely used Random Forest (RF) model. The findings are as follows: (1) the incidence of Benggang erosion is rising initially with an increase in elevation, slope, topographic wetness index, rainfall erosivity, and fractional vegetation cover, followed by a subsequent decline, highlighting its distinct characteristics compared to typical types of gully erosion; (2) the AUC values from the ROC curves for the Maxent and RF models are 0.885 and 0.927, respectively. Both models converge on elevation, fractional vegetation cover, rainfall erosivity, Lithology, and topographic wetness index as the most impactful variables; (3) both models adeptly identified regions prone to potential Benggang erosion. However, the Maxent model demonstrated superior spatial correlation in its susceptibility assessment, contrasting with the RF model, which tended to overestimate the BES in certain regions; (4) the Maxent model’s advantages include no need for absence samples, direct handling of categorical data, and more convincing results, suggesting its potential for widespread application in the BES assessment. This research contributes empirical evidence to study the Benggang erosion developing conditions in the hilly regions of southern China and provides an important consideration for the sustainability of the regional ecological environment and human society.

Dynamic Changes and Influencing Factors Analysis of Groundwater Icings in the Permafrost Region in Central Sakha (Yakutia) Republic under Modern Climatic Conditions

Dynamic Changes and Influencing Factors Analysis of Groundwater Icings in the Permafrost Region in Central Sakha (Yakutia) Republic under Modern Climatic Conditions

Interpretative structural modeling method: An analysis of influencing factors analysis on the adverse experiences of cardiac surgery patients in the intensive care units

ObjectiveTo construct an interpretation structure model of adverse experiences of cardiac surgery patients in intensive care unit, so as to provide a reference for optimizing the experience of critical patients step by step. MethodsLiterature review, semi-structured interviews, questionnaires and Delphi method were used to summarize and analyze the influencing factors of intensive care experience in cardiac surgery. The explanatory structural model was used to divide the influencing factors into levels and construct the explanatory structural model of adverse experience of cardiac surgery patients in intensive care. ResultsA hierarchical structure model containing 34 elements and 15 levels was constructed, which were divided into Surface level, middle level and root level. ConclusionThe intensive care experience of patients in cardiac surgery department is mainly affected by 34 factors. There are direct or indirect correlations between the influencing factors, and different levels have different effects.

Analysis of influential factors on additional shear force induced by shear deformation in girder bridges with corrugated steel webs

Girder bridges with corrugated steel webs (CSWs) exhibit critical shear regions proximate to supports, where the CSWs are subjected to peak shear forces and bending moments potentially inducing shear buckling. This study explores the shear behavior within critical regions experiencing hogging bending moments, where the accordion effect of CSWs dramatically exacerbates shear deformation. However, due to the substantially reduced shear stiffness of CSWs relative to concrete slabs and crossbeams, the shear deformation of CSWs is constrained, causing additional shear forces to form within them. The traditional basic shear method proves inadequate in predicting shear stress within CSWs for it omits these additional shear forces. The study introduces an enhanced formula to precisely assess shear stresses within CSWs, particularly in hogging moment regions of girders, considering the additional shear force induced by CSWs' shear deformation. A strong correlation between the additional shear forces and the geometric characteristics of CSWs has been identified through theoretical analysis. Accordingly, the study systematically investigates the impact of various parameters on the additional shear force through finite element parametric analysis. Comparative analysis demonstrates that the CSWs’ thickness, the height-to-span ratio, and the boundary constraint conditions of girders significantly impact additional shear forces, thereby affecting the shear transfer efficiency within the cross-section of the girder.