Multi-factor Influence Research Articles

Research on Core technology R&D Investment and Outcome Transformation Strategy of Single Manufac-turer Based on Multi-factor Influences

Research on Core technology R&D Investment and Outcome Transformation Strategy of Single Manufac-turer Based on Multi-factor Influences

Numerical simulation of multi-factor influence mechanism in “Barrier closure” dust removal technology

A novel “Barrier closure” method is introduced in this study to address the challenges of sealing dust sources in open workplaces, rapid dust diffusion, and difficulty in controlling dust pollution. The coupling model of airflow and dust is applied to analyze the influence of various factors on dust diffusion. The results show that before using the new technology, the dust concentration at the sidewalk exceeded 350 mg/m3 due to the movement of the dusty airflow. After using the new technology, the airflow flows to the sidewalk under the blocking action of the baffle, resulting in higher mechanical energy of the airflow on the sidewalk side than the coal wall side. Thus, inhibits the movement of the dusty airflow to the sidewalk area. In the case of downwind and upwind coal mining, the dust concentration in the sidewalk area is controlled below 50 mg/m3, which is 94.2 % and 55.6 % reduction, respectively, compared to the absence of the new technology. When the wind-regulation baffle is 3.6m from the rear drum of the coal cutter, the dust concentration in the working area is controlled within 20.0 mg/m3, and the dust removal efficiency reaches 94.2 %. When the inlet wind velocity is 2.0 m/s, the dust concentration along the sidewalk is less than 100 mg/m3, the dust concentration in the operation area is less than 20.0 mg/m3, and the dust removal efficiency is 94.5 %. This study provides a novel approach for establishing clean working environments.

Bicharacteristic probability of detection of crack under multi-factor influences using alternating current field measurement technique

Alternating Current Field Measurement (ACFM) technique has been widely used in various fields such as oil & gas, aerospace areas. However, during the detection process, the probability of detection (POD) is influenced by multi-factors such as lift-off height, scanning angle and detection speed. In this paper, a bicharacteristic probability of detection (BPOD) model is developed for quantifying the crack detection reliability using integrated Bx and Bz signals in the ACFM technique. A multidimensional BPOD model is established to study the influence of multiple factors on the detection probability of cracks using ACFM technique. A Bayesian network-based method is proposed to reverse the hierarchical impact weights of influence factors on the BPOD of the ACFM technique. The results show that the BPOD model considering both Bx and Bz response signals can evaluate smaller defects than the conventional POD model. The multiple influencing factors can substantially decrease the BPOD of cracks. The hierarchical impact weights on BPOD for cracks are ranked as follows: lift-off height, detection speed, personnel involved in the detection process and scanning angle. This approach can retrospectively find the potential contributors to the missed detections based on the BPOD.

Key Factors in Home Buying Decisions Quality, Price, and Location

This study examines the impact of product quality, price, and location on home buying decisions in Bumi Papan Selaras Housing, Sidoarjo, Indonesia. Utilizing a quantitative survey method with 96 respondents, the research analyzes data using various statistical tests in SPSS version 26. Results reveal that product quality, price, and location significantly influence home purchasing choices both individually and collectively. These findings underscore the importance of considering multiple factors simultaneously in housing market dynamics, aiding developers and policymakers in catering to consumer preferences effectively and promoting sustainable urban development. Highlight: Comprehensive Study: Investigates multifactor influence on home purchases. Consumer Preferences: Analyzes product quality, price, and location significance. Methodological Rigor: Employs survey method for accurate data collection. Keywoard: Home Buying Decisions, Product Quality, Price, Location, Survey Method

A Multi-Point Joint Prediction Model for High-Arch Dam Deformation Considering Spatial and Temporal Correlation

Deformation monitoring for mass concrete structures such as high-arch dams is crucial to their safe operation. However, structure deformations are influenced by many complex factors, and deformations at different positions tend to have spatiotemporal correlation and variability, increasing the difficulty of deformation monitoring. A novel deep learning-based monitoring model for high-arch dams considering multifactor influences and spatiotemporal data correlations is proposed in this paper. First, the measurement points are clustered to capture the spatial relationship. Successive multivariate mode decomposition is applied to extract the common mode components among the correlated points as spatial influencing factors. Second, the relationship between various factors and deformation components is extracted using factor screening. Finally, a deep learning prediction model is constructed with stacked components to obtain the final prediction. The model is validated based on practical engineering. In nearly one year of high-arch dam deformation prediction, the root mean square error is 0.344 and the R2 is 0.998, showing that the modules within the framework positively contribute to enhancing prediction performance. The prediction results of different measurement points as well as the comparison results with benchmark models show its superiority and generality, providing an advancing and practical approach for engineering structural health monitoring, particularly for high-arch dams.

An Investigation into the Multifactor Influences on Ecotourism Behavioural Intention, with Satisfaction as a Mediating Variable

This study examines the multifactor influencing ecotourism behavioural intention in China from the perspective of ecotourism satisfaction. The study distributed a self-filled questionnaire to 400 tourists who had visited a famous scenic ecotourism spot in China. Use PLS-SEM for structural equation modelling and analysis. The survey results indicate that tourist satisfaction only mediates perceived values, attitudes, and ecotourism behavioural intentions. Subjective norms, perceived value, attitude, and perceived behavioural control all impact tourist satisfaction. This provides theoretical support and management direction for China’s sustainable and healthy ecotourism development.

Advancing portfolio optimization: The convergence of machine learning and traditional financial models

This paper delves into the transformative impact of machine learning (ML) on portfolio optimization, showcasing how ML algorithms can significantly enhance traditional financial models such as the Capital Asset Pricing Model (CAPM) and the Arbitrage Pricing Theory (APT). Through a comprehensive examination of regression analysis, classification algorithms, and reinforcement learning, we illustrate the methodologies by which ML refines the prediction of asset returns, assesses investment risks, and dynamically adjusts portfolio allocations. We discuss the integration of ML with CAPM and APT to improve the estimation of systematic risk and identify multi-factor influences on asset returns, offering a more nuanced approach to optimizing portfolios. Additionally, the paper highlights the role of big data in augmenting predictive accuracy and the application of optimization algorithms like Gradient Descent and Genetic Algorithms in achieving optimal asset allocations. By addressing challenges such as multicollinearity and overfitting, we demonstrate the potential of ML to revolutionize investment strategies, enabling more sophisticated risk management and return maximization. This study not only underscores the synergy between ML and traditional financial theories but also paves the way for future innovations in financial analytics.

Poisson's ratio testing method and multi-factor influence quantification model of asphalt pavement materials

The current standard in China does not specify the standard test method for determining Poisson's ratio, and the Poisson's ratio used in pavement design is often roughly fixed, which can result in large errors in the calculations and analysis of road structure. In this paper, the research on different test methods for Poisson's ratio is carried out and the non-linear characteristics of tensile and compressive Poisson's ratio on pavement materials under different influencing factors are analyzed. The results indicate that the absolute value of the ratio between transverse rebound strain and longitudinal rebound strain under 50% of the maximum destructive load can objectively characterize the mechanical properties of Poisson's ratio, and it is the optimal measurement method for Poisson's ratio in asphalt pavement materials. The Poisson's ratio of two pavement materials both decreases with the increase of loading rate and gradually tends to a constant value. Cement dosage and temperature are the most significant factors affecting the tensile and compressive Poisson's ratio of cement stabilized crushed stone and asphalt mixture, respectively. Specifically, when cement dosage increases by 1%, the Poisson's ratio of cement stabilized crushed stone decreases by approximately 10%, and the temperature has a significant influence on the difference in tension and compression for Poisson's ratio of asphalt mixture (up to 40%). The asphalt content corresponding to the minimum Poisson's ratio is basically consistent with the optimal asphalt content obtained by the Marshall volume method. Based on these, a quantification value-taking model is established of the tensile and compressive Poisson's ratios of typical asphalt pavement materials varying with multiple factors. The research results can serve as a reference for the scientific value-taking of pavement structural design parameters considering the different mechanical characteristics of materials in tension and compression.

How Does Public Service Motivation Affect the Proactive Service Behaviors of Grid Workers? A Study of Survey Evidence from Eastern China.

In China, grid workers have increasingly become an indispensable and important force in basic social governance. They not only undertake several tasks, such as gaining publicity, collecting information, resolving conflicts, and assisting in management, but they also actively serve the grid residents enthusiastically and engage in proactive service behaviors. In order to better cultivate this important force, we hope to have a better understanding of the factors contributing to the behavioral performance of grid workers, especially the impact of organizational and personal factors. In this study, we sought to establish what factors influence the proactive service behaviors of grid workers. Based on a theoretical consideration of factors such as public service motivation, occupational identity, and organizational climate, a multi-factor influence hypothesis model was constructed to explain the proactive service behaviors of these workers. By analyzing data based on 348 paired survey samples received in two stages in eastern China, these hypotheses were then tested. The results reflect that grid workers' public service motivation can stimulate proactive service behaviors. Furthermore, occupational identity plays a mediating role, while organizational support and organizational service climate play a positive moderating role between public service motivation and occupational identity. This finding clarifies the important influencing factors of proactive service behaviors among grassroots workers, such as grid workers, and has important implications for how to effectively motivate these groups to provide more proactive services, promoting their sustainable development and improve the effectiveness of grassroots governance.

Analysis of Aging Characteristics of Umbrella Skirts of Composite Insulators Operating under the Influence of a Wind and Sand Environment in South Xinjiang.

To study the influence of multi-factors, such as long sunshine, sand erosion, and so on, in southern Xinjiang, we selected two kinds of composite insulators for the transmission lines in southern Xinjiang to study the aging characteristics of the umbrella skirt surface. The results of scanning electron microscope (SEM) and X-ray photoelectron spectroscopy (XPS) show that the surface roughness of the umbrella skirt is high and there are a large number of micron inorganic particles. The skirt has the characteristics of low C/O element ratio and high Al element content. The results of thermogravimetric analysis and micro infrared test show that the aging depth of the Myli Line skirt after 19 years of operation is 160~190 µm and that of Yuhe Line 1 after 14 years of operation is greater than 180 µm. The plasma discharge method was used to simulate the corona discharge in the actual operation to accelerate the aging of the surface of the umbrella skirt and the hydrophobic recovery of the umbrella skirt was investigated. The results show that the temperature has a great influence on the surface hydrophobic recovery performance after plasma treatment. These results may provide some theoretical guidance and technical support for the selection, operation, and maintenance of composite insulators in Xinjiang.

Comprehensive Modeling and Safety Protection Strategy for Thermal Runway Propagation in Lithium-Ion Battery Modules under Multi-Factor Influences

This paper addresses the challenge of thermal runaway propagation in lithium-ion battery modules and presents a safety protection design method based on a thermal propagation model. Firstly, it systematically analyzes the triggering mechanisms of thermal runaway in batteries, establishes a model for cell thermal runaway, and calibrates the model parameters through experiments. Secondly, by integrating the cell thermal runaway model and considering the three-dimensional structure of the battery module, a comprehensive thermal runaway propagation model is developed and validated. Subsequently, a simulation study on thermal runaway propagation, incorporating multi-factor influences and typical operating conditions, is conducted using the established thermal propagation model for the battery module. The study elucidates the thermal runaway propagation characteristics of the battery module under different safety protection strategies. The findings highlight that the proposed safety protection strategy effectively mitigates thermal propagation within the battery module, particularly when the thermal runaway is influenced by multiple factors.

A Study on the Relationship between Multiple Regression Analysis and Students’ Achievement Improvement in Higher Education English Teaching

Abstract Students’ English learning achievement is affected by many factors, and the study found that there is a significant correlation between the English teaching process on the improvement of students’ English achievement, especially the teaching design. Based on this, this paper analyzes the correlation between English teaching design and students’ academic performance improvement in colleges and universities using the multiple linear regression method. Based on the multiple covariance problem in the multiple linear regression model gives the two-parameter algorithm optimization at the α level, and finally applies the optimized multiple linear regression model by example, and gets the conclusion that the optimized multiple linear regression model with the two-parameter algorithm optimization at the α level is feasible. The main research results are as follows: after this paper improves the parameter algorithm of the model on the α level, it is found that the fit of the model also reaches 85% under the influence of multifactor (more than 5), which proves that this paper is feasible for the improvement of the two parameters on the α level. The t-values of the independent test results for the five dimensions of teaching objectives and content are 6.765, 4.792, 3.163, 5.226, and 4.123, respectively, and their significance is less than 0.01, which proves that there is a significant correlation between the five dimensions selected in this paper about the design of English language teaching and the improvement of the English language learning achievement. The predictor variables chosen for the regression process are able to explain the response variables to a large extent, so the model can be used to predict improvement in English achievement.

From Tradition to Modernity: The Transformation of Chinese Early Childhood Physical Education under Multi-Factor Influences

From Tradition to Modernity: The Transformation of Chinese Early Childhood Physical Education under Multi-Factor Influences

Estimation of Iron Loss in Permanent Magnet Synchronous Motors Based on Particle Swarm Optimization and a Recurrent Neural Network

The popularity of permanent magnet synchronous motors (PMSMs) has increased in recent years due to their high efficiency, compact size, and low maintenance needs. Calculating iron loss in PMSMs is crucial for designing and optimizing PMSMs to achieve high efficiency and a long lifespan, as this can significantly affect motor performance. However, multiple factors influence the accuracy of iron loss calculations in PMSMs, including the intricate magnetic behavior of the motor under different operating conditions, as well as the influence of the motor’s dynamic behavior during the operation process. This paper proposes a method based on particle swarm optimization (PSO) and a recurrent neural network (RNN) to estimate the iron loss in PMSMs, independent of the empirical iron loss formula. This method establishes an iron loss calculation model considering high-order harmonics, rotating magnetization, and temperature factors. Accounting for the multifactor influence, the model studies the law of loss change under different magnetic flux densities, frequencies, and temperature conditions. To avoid the deviation problem caused by conventional polynomial fitting, a multilayer RNN and PSO are used to train and optimize the neural network. Iron loss in complex cases beyond the measurement range can be accurately estimated. The proposed method helps achieve a PMSM iron loss calculation model with broad applicability and high accuracy.

An Intelligent Evaluation Method for Service Safety of Cable Net Structures under Multiple Factors

Various uncertainties often influence the serviceability of cable net structures, which can impact their structural safety performance. The accurate identification of sensitive factors during the structure’s service life and the determination of its serviceability state is crucial for achieving intelligent serviceability safety. In this paper, based on the digital twin model, a multi-factor-based assessment method for the serviceability safety of cable net structures was proposed. Firstly, the assessment method for serviceability safety under multi-factor influence was described in detail, outlining the specific workflow. Secondly, key indicators that affect the structural safety state are selected, and their range of variation is determined. Subsequently, a comprehensive dataset of sample data, considering long-term multi-factor influence, was constructed, through combined simulation using MATLAB 2021 and ANSYS 15.0. Finally, a support-vector-regression-based structural safety assessment model was established and validated.

Effect of Multifactor on the Stability of Glucose Solution Emulsified Heavy Fuel Oil.

Water emulsified heavy fuel oil (HFO) has been a promising alternative fuel for reducing oil consumption and preventing environmental pollution. However, the intrinsic challenges such as fuel formula, emulsion stability, and preparation process normally limit its further applications in energy-saving and emission reduction applications. In this study, the glucose obtained from biomass was added to a dispersed-phase aqueous solution of water emulsified HFO to prepare a novel alternative emulsified fuel. First, based on the preliminary experimental design, the effects of glucose and surfactant on the stability of the HFO emulsion were systematically evaluated through the appearance of emulsion separation, droplet size distribution, and rheological characteristics. It indicated that the surfactant ratio, hydrophilic-lipophilic balance value, solution ratio, and glucose/water ratio had significant impacts on emulsion stability. Subsequently, the optimum range of influencing factors of emulsion stability was determined by a single factor experiment and determined by the response surface methodology based on the Box-Behnken design; the optimal values of the above factors were 2.439 v/v%, 5.807, 26.462 v/v%, and 35.729%, respectively. Under these conditions, an optimal glucose solution emulsified HFO with a uniform brown color and long-term stability was obtained, making the unseparated emulsion ratio reach 98% (lasting for 7 days at 85 °C). Meanwhile, it emerged that the influence of multifactor on emulsion stability was not a simple linear correlation, and there were significant interactions between the solution ratio and the surfactant ratio, as well as between the glucose/water ratio and the surfactant ratio.

The multi-factor influence of frost resistance and prevention effect of tunnel thermal insulation layer in cold areas

To figure out the thermal insulation effect of tunnel thermal insulation layer under the action of different factors in cold areas, the heat transfer analysis module in ABAQUS software was used for the Jiangluling Tunnel to numerically simulate the temperature change of the lining. The said thermal insulation effect under different insulation layer laying methods, insulation material heat conductivity coefficients, insulation layer thickness values, and air temperature conditions was analyzed based on the range of Heat Regulation Circle (HRC), and the empirical equation proposed by Japanese Scholar Kurokawa Yoshinori was employed to calculate the laying length of thermal insulation layer, which was then verified by the safety coefficient of tunnel lining structure. The research results showed that, after the thermal insulation layer had been set, the radial depth of annulus of HRC decreased significantly, indicating that the convective heat transfer of cold air had been suppressed; from high to low, the thermal insulation performance of thermal insulation layer installed by different laying methods was: double-layer style, wall-attaching style, composite filler style, and wall-detaching style. When installing thermal insulation layer for the Jiangluling Tunnel, the wall-attaching style thermal insulation layer is suggested; the thickness of thermal insulation layer should not be less than 5.5 cm; the thermal insulation material should take a thermal conductivity coefficient between 0.025 and 0.04 (W/m.k) and be laid throughout the entire tunnel; when the temperature drops below −17 °C, extra thermal insulation measures should be taken to ensure the frost resistance and prevention ability of the tunnel structure.

Precipitation and vegetation transpiration variations dominate the dynamics of agricultural drought characteristics in China

Agricultural drought posing a significant threat to agricultural production is subject to the complex influence of ocean, terrestrial and meteorological multi-factors. Nevertheless, which factor dominating the dynamics of agricultural drought characteristics and their dynamic impact remain equivocal. To address this knowledge gap, we used ERA5 soil moisture to calculate the standardized soil moisture index (SSI) to characterize agricultural drought. The extreme gradient boosting model was then adopted to fully examine the influence of ocean, terrestrial and meteorological multi-factors on agricultural drought characteristics and their dynamics in China. Meanwhile, the Shapley additive explanation values were introduced to quantify the contribution of multiple drivers to drought characteristics. Our analysis reveals that the drought frequency, severity and duration in China ranged from 5–70, 2.15–35.02 and 1.76–31.20, respectively. Drought duration is increasing and drought intensity is intensifying in southeast, north and northwest China. In addition, potential evapotranspiration is the most significant driver of drought characteristics at the basin scale. Regarding the dynamic evolution of drought characteristics, the percentages of raster points for drought duration and severity with evapotranspiration as the dominant factor are 30.7 % and 32.7 %, and the percentages with precipitation are 35.3 % and 35.0 %, respectively. Precipitation in northern regions has a positive effect on decreasing drought characteristics, while in southern regions, evapotranspiration dominates the dynamics in drought characteristics due to increasing vegetation transpiration. Moreover, the drought severity is exacerbated by the Atlantic Multidecadal Oscillation in the Yangtze and Pearl River basins, while the contribution of the North Atlantic Oscillation to the drought duration evolution is increasing in the Yangtze River basin. Generally, this study sheds new insights into agricultural drought evolution and driving mechanism, which are beneficial for agricultural drought early warning and mitigation.

Factors influence analysis and life cycle assessment of innovative bifacial photovoltaic applied on building facade

Bifacial PV (BPV) could use both radiation on the front and back for power generation. The challenge is that reflected radiation absorbed by the back is limited when BPV is applied on building. BPV cannot be effectively utilized. In this research, BPV was innovatively combined with reflective film to form BPV curtain wall system, which is conducive to higher power output and could be applied to the facade of building directly. Compared with mono-facial PV, power generation of the system could be increased by 24% on average. Life cycle assessment (LCA) was used to analyze the influence of multi-factors on this system, which makes the evaluation of the performance more comprehensive. It could be found that the energy and greenhouse gas payback time could reach the shortest when the system is faced South. The results of the system become better with the increase of photovoltaic coverage, but this trend will gradually slow down when the coverage is greater than 75%. Besides, when the distance with reflective layer is about 1/12 L, the system has the shortest payback time. This research explored a new form of BPV system, and has beneficial effect on the field of application of BPV in buildings.

Preliminary experimental investigation on long-term fracture conductivity for evaluating the feasibility and efficiency of fracturing operation in offshore hydrate-bearing sediments

Gas hydrate is undoubtedly an essential resource for sufficient energy supply (natural gas) in the near future. Unfortunately, low production efficiency limits its effective development and utilization. Hydraulic fracturing is a common and effective engineering measure to stimulate oil and gas reservoirs, as well as enhance oil recovery. However, its feasibility for clayey silt hydrate-bearing sediments is unclear now. What is more, there is no scientific basis for determining or assuming the value of fracture conductivity used in productivity prediction. In the present work, factors affecting conductivity of fractures in hydrate-bearing sediments were explored first. The experimental results showed that the conductivity of fracture in hydrate-bearing sediments was lower than 50 μm2·cm. Besides, it was found that the increase of proppant size, proppant concentration and hydrate saturation can improve the fracture conductivity. However, to achieve a similar effect, the closure pressure needs to be reduced appropriately. Based on these experiment results, the multi-factor influence model of fracture conductivity in hydrate-bearing sediments was developed. The conductivity profile at site SH2 in the northern South China Sea was then obtained. Finally, feasibility and efficiency evaluation of fracturing operation in hydrate reservoir was numerically conducted. The investigation results reveals that fracturing operation can greatly promote hydrate dissociation and gas production in hydrate development process. In addition, the importance of accurately determining fracture conductivity in productivity prediction of hydrate reservoir was also explored. It was found that gas production is only 12.11 × 108 m3 when fracture conductivity is 5.0 μm2·cm. However, it was as high as 22.82 × 108 m3 when fracture conductivity is 40 μm2·cm, which is 1.88 times that of the former case. The present work provides new perspective for design and optimization of fracturing operation in hydrate-baring sediments.