Comprehensive Influence Factors Research Articles

Heat transfer characteristic and optimization design of alter diameter array impingement cooling structure

The fine design of blade cooling is one of the leading directions in the development for blade manufacturing technology, attaining optimal and consistent cooling efficiency is the common research concern in the field of fine design. In the current work, the alter diameter hole arrays are applied to reasonably regulate the distribution of cold air flow, which aims to achieve uniform cooling performance of impinging cooling technology. Experiment involving temperature sensitive paint technology and simulation based on SST k-w turbulence model are conducted. Subsequently, the mechanism of cooling performance increasement is investigated; the impact of structural elements on the heat transfer properties is discussed. Moreover, the correlation between the Nu number of the target surface and the comprehensive influence factors was fitted based on response surface method; the optimal structure in the range of the structural parameters is calculated based on NSGA-II and TOPSIS method. The results reveal that the influence of d and x on average surface Nusselt number is remarkable. In addition, the response surface model fitting model is accurate to predict the structure parameter effect on cooling performance of alter impingement cooling, with the prediction error less than 2.67 %. Compared with the basic EDI, the Nu of the ADI structure with ∇T constraint is increased by 5.2 %, the temperature inhomogeneity is reduced by 53.4 %. The significant improvement of temperature inhomogeneity indicates the practicality of ADI structure and optimized procedure used in improving impingement cooling efficiency which shows promising performance increasement.

Artificial intelligent housekeeper based on consumer purchase decision: a case study of online E-commerce

PurposeThe rise of artificial intelligence (AI) and machine learning has largely promoted the emergence of “autonomous decision-making” (ADM). This paper aims to establish a personalized artificial intelligent housekeeper (AIH) that knows more about our hobbies, habits, personality traits, and shopping needs than ourselves and can replace us to do some habitual purchasing behavior.Design/methodology/approachWe propose an AI decision-making method based on machine learning algorithm, a novel framework for personalized customer preference and purchase. First, the method uses interactive big data to predict a potential consumer’s decision possibility. Then, the method mines the correlation between consumer decision possibility and various factors affecting consumer behavior. Finally, the machine learning algorithm is used to estimate the consumer’s purchase decision according to the comprehensive influencing factors data of the target consumer.FindingsThe experimental results show that the method can predict the regular consumption behavior of consumers in advance and make accurate decision-making behavior. It can find correlations from a large amount of data to help predict many simple purchase decisions in our life, and become our AIH.Originality/valueThis study introduces a new approach that not only has the auxiliary decision-making function but also has the decision-making function. These findings contribute to the research on automated decision-making process of AI and on human–technology interaction by investigating how data attributes consumer purchase decision to AI.

Study of summer microclimate and PM2.5 concentration in campus plant communities

Understanding the influencing effect of meteorological factors and air pollutants in the campus plot and the relationship between them is an important topic in the planning and design of campus green space. The changes of pollutant concentrations and meteorological factors in campus green space have certain patterns and specific influencing factors. In this study, we selected four sample plots in Nanjing Forestry University as the research objects, and collected various environmental parameters of the four plots on July 25, 2022. The results showed that the main influences of meteorological factors are the type of the underlying surface of the site, the degree of plant canopy density and the shade coverage area of the building. These factors mainly have a great influence on the value of temperature and humidity. The comprehensive influencing factors can be concluded that the cooling and humidifying effect of the site is ranked as follows: forest > lawn > asphalt road > concrete Square. The main influencing factors of pollutants are: illumination, wind speed, temperature and relative humidity. Among them, illumination and temperature have a negative correlation with PM2.5, wind speed and relative humidity have a positive correlation with PM2.5. Our research shows that the adjustment of campus green space factors can reduce the concentration of pollutants by changing the meteorological factors.

Spatiotemporal Variation and Stability of Rice Planting Using Landsat–MODIS Fusion Images from 1990 to 2020

Dongting Lake Plain is a historic foundation for China’s commodity grain production. We used Landsat images to interpret the rice planting pattern from 1990 to 2020 based on the vegetation index curve and crop time window differences. The research aims included the spatiotemporal change in the rice planting area and the multiple cropping index, the transformation properties between single-crop and double-crop, and influence factors of rice cultivation. The findings indicated that the rice planting area has increased by 23.64% over the past 30 years. However, the multiple cropping index decreased by 17.39%. The area of single-crop rice increased by 2.6 times, while the area of double-crop rice decreased by 23.19%, which indicated that the planting intensity of rice has decreased. The area where rice has been steadily planted for 30 years is approximately 5600 km2, accounting for 87% of all rice planting land in this study area. The transformation from double-crop rice to single-crop rice was the most obvious characteristic of internal changes. The marginal benefits of current agricultural policies have decreased. This research may provide a theoretical basis for the refined management of rice and improve agricultural policies. More clouds in the remote sensing image limited the time resolution. Future research may further explore the comprehensive influencing factors.

Analysis of comprehensive influencing factors of retired fisherman under the background of ten-year fishing ban

Analysis of comprehensive influencing factors of retired fisherman under the background of ten-year fishing ban

Performance audit evaluation of marine development projects based on SPA and BP neural network model

Abstract Accurately grasping the comprehensive cost-benefit and influencing factors of various marine development projects is of great significance to the promotion of high-quality marine development. This work screened and constructed the performance audit evaluation index system of comprehensive cost-benefit of marine development projects from the four aspects including society, economy, resources, and environment. The entropy weight set pair analysis (SPA) was used to audit and evaluate the comprehensive cost-benefit of 15 marine development projects during the construction and operation period. The back propagation (BP) neural network model was used to test the results. The main influencing factors were analyzed by the grey relational analysis (GRA) model. It was found that the comprehensive cost-benefit performance audit evaluation index of the six projects of marine protected area and offshore wind power were higher than the other nine projects of mariculture, sewage dumping, and port industry. The main influencing factors were economic net income, loss of wetland ecological service value, environmental pollution prevention and control cost, new addition employment rate, etc. The countermeasures suggestions were put forward.

Underfrequency Load Shedding Strategy With an Adaptive Variation Capability for Multi-Microgrids

Multi-microgrids (MMGs) suffer from power shortages due to the loss of utility grid support when an unintentional transition occurs. This imposes a transient shock on the system voltage and frequency. To maintain the frequency stability and power balance of an islanded MMG, this paper presents an underfrequency load shedding (UFLS) strategy with adaptive variation. A comprehensive load evaluation method based on a composite least squares support vector machine (CLS-SVM) is proposed to ensure uninterrupted power for critical loads. This method considers the comprehensive evaluation influence factors (CEIFs) of loads. Then, a least squares support vector machine (LS-SVM) provides the load shedding determination factors, transforming the problem of determining critical loads into a 0-1 planning problem. A method with adaptive variation is proposed to solve the UFLS model. The effectiveness of the proposed strategy is verified for an MMG model based on a modified IEEE 33-bus system. The test results show that: 1) the average accuracy of the proposed method is 21.05% higher than that based on LS-SVM; 2) compared with UFLS strategies based on the load level alone and on an intelligent algorithm, the frequency fluctuation range of the proposed strategy is 12.50% and 19.23% lower, respectively, and the frequency recovery time is 3.90% and 5.73% shorter, respectively; 3) compared with PSO, GOA and GA, the standard deviation of the iterative mean of the proposed algorithm decreases by 36.22%, 53.42%, and 34.00%, respectively. The proposed strategy can reduce the frequency fluctuation range and frequency recovery time while maintaining strong adaptability.

Thermal degradation of cellulose and coal gangue based on lumped reaction model and principal component analysis

The effect of cellulose on pyrolysis characteristics of coal gangue was studied based on TG-FTIR experiment. Cellulose with different mass ratios is added to coal gangue. Synergistic effect between cellulose and coal gangue on mass loss was observed in this study. CG1CEL1 and CG1CEL3 samples have mutual promotion effect between CG and CEL. Conversely, the inhibitory effect of CG3CEL1 between CG and CEL was evident at high temperature. The reaction mechanism of coal gangue and cellulose co-pyrolysis based on lumped reaction model was determined. Furthermore, the comprehensive influencing factors of coal gangue and cellulose co-pyrolysis were analyzed by principal component analysis. There is little research on the influence mechanism of cellulose, the main component of biomass, on the pyrolysis of coal gangue. This paper is of great significance to clarify the mechanism of co-pyrolysis of biomass and coal gangue. This study provides a basis for revealing the influence mechanism of cellulose on coal gangue pyrolysis, which accelerates the process of carbon neutralization. This can promote resource disposal of coal gangue by reduction, so as to reduce environmental pollution and improve the utilization rate of fossil energy.

A Review of Mathematical Logical Reasoning Literacy in China

Logical reasoning is an effective method to analyze and solve problems, obtain mathematical conclusions, and build a mathematical system. It is also the basis for forming a methodical, logical quality of thinking and rational spirit, so logical reasoning literacy has become a focus in educational studies. This paper summarizes the relevant research results and draws the following conclusions: Firstly, the previous research on logical reasoning literacy mainly focuses on four aspects: the connotation, level, influencing factors, and training strategies. Secondly, among them, level status and training strategies are hot topics in current research. Thirdly, regarding research methods, scholars mostly use test questions, questionnaires, and interviews to study the status. Fourthly, the single research method, lack of empirical research, unsystematic research on influencing factors, and unfeasible research conclusions on training strategies are deficiencies in previous research. Finally, the current research on logical reasoning literacy lacks theoretical research and balanced grade research. Therefore, it is necessary to further improve the research method in future studies and conduct a more in-depth and systematic study on the influencing factors from an empirical perspective, to find out more comprehensive influencing factors and more operable cultivation strategies.

A Novel Short-Term Power Load Forecasting Method Based on TSNE-EEMD-LSTM

In this paper, a novel power load forecasting model is proposed to fully extract the periodic characteristics of short-term load at various time scales and explore the potential correlations between influencing factors and characteristics of load components. Firstly, the t-distributed stochastic neighbor embedding algorithm is used to map sample points of high-dimensional load influencing factors to low-dimensional space, and the ensemble empirical mode decomposition algorithm is employed to split the historical load curve into multiple signal components with different frequencies. Then, several long short-term memory networks including nonlinear mapping and time series models are established to mine the relationship between low-dimensional comprehensive influencing factors and each intrinsic mode function component by utilizing different inputs. Finally, the effectiveness of the hybrid model is verified via using the short-term load dataset of 3-hour data granularity in a certain region, and the influence of key parameters of the model on the forecasting effect is discussed.

Study on the influencing factors and the spatiotemporal heterogeneity of Urban Heat Island effect in Nanchang City of China

ABSTRACT Although some researchers have explored spatiotemporal characteristics of the land surface temperature (LST) and analyzed how the influencing factors impact the LST, the comprehensive influencing factors and specific spatial relationships that explore the Urban Heat Island (UHI) effect from long time scale have rarely received widespread attention. Taking Nanchang as an example, this paper first uses Landsat data to retrieve the LST in years 2000, 2010, and 2019. Then, the Geographically Weighted Regression (GWR) model is utilized to investigate the various influencing factors. The results showed as follows: (1) The UHI effect was intensified in the past 20 years in Nanchang City. From 2000 to 2019, the “heat island” increased from 63.06% to 64.07%, while the “green island” decreased from 36.94% to 35.93%. (2) The spatiotemporal anisotropy of the UHI effect was significant. (3) The GWR model could better reveal the spatial heterogeneity of the factors affecting the UHI effects. The relationship between Soil Brightness Index (NDSI), Normalized Difference Built-up Index (NDBI), and Night-lighting (NL) and the relative UHI effect intensity had significant spatial non-stationarity. This study can provide some references from reasonable urban planning to alleviate the urban heat island.

Immune Algorithm to Suppress Rumor Propagation Based on Influence Maximization

In order to effectively reduce the spread range of rumor information, an immune method based on two-stage influence maximization to suppress rumor propagation is proposed. Firstly, User and Clustering Influence Maximizing (UCIM) algorithm is proposed to obtain the most influence node set at the current moment in the initial stage of event evolution based on the network topology and user characteristics. Secondly, the node set is identified and classified based on the RNN rumor detection model, and Immunosuppression Strategy considering the Average Path Weights and User-Clustering (IS-APWUC) strategy is proposed for the identified rumor nodes. In this strategy, rumor node set is taken as root nodes, and neighbor nodes with weak influence are pruned to construct an effective rumor path tree. Thirdly, considering the total probability of being infected by the rumors and the comprehensive influence factors, the nodes with high influence in the propagation stage are calculated as immune nodes so as to block the spread of the rumor information. Finally, the proposed method is verified by experiments on four real-world datasets. The results show that the IS-APWUC method has a better rumor suppression effect than the similar correlation algorithms.

Research on Data Analysis Literacy of High School Students in China

Data analysis is an important mathematical technique for studying random phenomena and a major method for mathematical applications in the era of big data. For students, the stage of high school is a critical period to form and develop their data analysis ability, so scholars have paid a lot of attention to the issue of education about data analysis literacy of high school students. However, there is a lack of systematic organization of the literature. In order to help researchers grasp the current status of research and promote further investigation, this paper summarizes relevant literature in China and analyzes the research results. The following conclusions are drawn from the summary and analysis of previous studies: 1. Previous studies on data analysis literacy of high school students have focused on five main aspects: basic connotation, cognitive status, influencing factors, cultivation strategies and evaluation methods; 2. Among them, cognitive status and cultivation strategies are the hot issues; 3. In terms of research methods, scholars mostly use pencil-and-paper tests, questionnaires or interviews to investigate cognitive status, and analyze the influencing factors and cultivation strategies just with theoretical thinking; 4. In previous studies, there are shortcomings of single research method and lack of empirical practice, while the previous researches on influencing factors are not systematic or comprehensive. The research conclusions on cultivation strategies lack feasibility or validity, and the researches on evaluation methods are relatively scarce. Therefore, scholars can start from the evaluation method to explore a reasonable and feasible evaluation system with high audience. It is necessary for scholars to further improve the research methods on the influencing factors and cultivation strategies in future studies, and to conduct more in-depth and systematic researches on the influencing factors of data analysis literacy from an empirical perspective, in order to find out more comprehensive influencing factors and more operable cultivation strategies.

Research on Mathematical Intuitive Imagination Literacy in China

In the process of discovering, formulating, analyzing, and solving problems, intuitive imagination is always an essential part, which is the thinking basis for exploring and forming argumentative ideas, conducting mathematical reasoning, and constructing abstract structures. It plays a vital role in students’ innovative consciousness and practical ability. Thus mathematical intuitive imagination literacy has become a focus in educational research. By its relevant research findings in China, the following conclusions can be drawn: (1) Previous research on mathematical intuitive imagination literacy mainly focused on five aspects: connotation, the academic profile of high school students, influencing factors, cultivation strategies, and evaluation methods. (2) Among them, connotation, the academic profile of high school students, and cultivation strategies of mathematical intuitive imagination literacy are the hot topics of current research. (3) In terms of research methods, scholars mainly used test questions, questionnaires, or interviews to investigate the academic profile of students, and most of them used theoretically critical thinking to analyze the influencing factors and cultivation strategies. (4) Previous research adopted a single research method and lacked empirical research; besides, the research on the influencing factors is not systematic and comprehensive enough. Meanwhile, the research conclusions on the cultivation strategies lack feasibility. Therefore, to find out more comprehensive influencing factors and more operable cultivation strategies, it is necessary to improve the research method further and conduct more in-depth and systematic research on the influencing factors of mathematical intuitive imagination literacy from an empirical perspective.

Study on Pre-service Mathematics Teachers’ Mathematical Critical Thinking Ability in China: A Review

As the pre-service mathematics teachers, their mathematical critical thinking ability influences students’ mathematical learning and innovative thinking. So, more and more researchers have begun to pay attention to the research on pre-service mathematics teachers’ mathematical critical thinking ability in China. The relevant results were summarized with the literature research method and the following conclusions are drawn: Firstly, the research on pre-service mathematics teachers’ mathematical critical thinking ability mainly focuses on the concept of mathematical critical thinking, the current situation of pre-service teachers’ ability, the influencing factors, and the cultivation measures. Secondly, the influencing factors and cultivation measures of mathematical critical thinking ability are the hot issues of current research. Thirdly, the research method is single. Most researchers just use theoretical thinking to analyze the influencing factors and cultivation measures. Fourthly, the suggestion about improving pre-service mathematics teachers’ mathematical critical thinking is a blank spot. Therefore, it is suggested to use more research methods on mathematical critical thinking ability to find more comprehensive influencing factors and more reasonable enhancement measures.

Analysis of Spatial Distribution of CVD and Multiple Environmental Factors in Urban Residents.

Cardiovascular disease (CVD) poses a serious threat to urban health with the development of urbanization. There are multifaceted and comprehensive influencing factors for CVD, so clarifying the spatial distribution characteristics of CVD and multiple environmental influencing factors is conducive to improving the active health intervention of urban environment and promoting the sustainable development of cities The spatial distribution characteristics of CVD deaths in a certain district, Bengbu City, Huaihe River Basin, China, in 2019 were explored, and the correlation between multiple environmental factors and CVD mortality was investigated in this study, to reveal the action mechanism of multiple environmental factors affecting the risk of mortality. Relevant studies have shown that (1) CVD deaths are characterized as follows: male deaths are more than females; the mortality is higher in those of higher age; most of them are unemployed; cardiocerebral infarction is the main cause of death; and the deaths are mainly distributed in the central city and near the old urban area. (2) The increased CVD mortality can be attributed to the increased density of restaurants and cigarette and wine shops around the residential area, the increased traffic volume, the dense residential and spatial forms, the low green space coverage, and the distance from rivers. Therefore, appropriate urban planning and policies can improve the active health interventions in cities and reduce CVD mortality.

Neuroplasticity induced by working memory training: A spatio-temporal model of decreased distribution in brain regions based on fMRI experiments

<p id="p00010">Working memory training (WMT) induces neuroplasticity, but its specific mechanism remains unclear. In order to explore the spatio-temporal characteristics of brain function changes induced by WMT, the findings of 37 fMRI papers on WMT in normal population over the last 20 years were investigated with a step-by-step approach, including 5 methods and the following 6 steps, as well as with two assumptions based on the two theories— “Extended Parieto-Frontal Integration Theory” (ExtPFIT) and “neuro efficiency hypothesis”. Step 1, the changes of activation pattern and function connectivity in various brain regions before and after WMT among the 37 papers were compared using descriptive review, frequency analysis and Chi-square test. It was found that 5 association areas, 7 macro regions and 3 sub-regions of the brain were changed by WMT. Especially, the number of articles reporting decreased activation in 3 sub-regions—superior frontal gyrus, inferior parietal lobule and cingulate gyrus was more than that reporting increased activation in them, and this difference was statistically significant. Step 2, 26 out of the 37 papers were used to conduct meta-analysis with activation likelihood estimation method, and it showed that 3 sub-regions with the statistically significant effects in their decreased activation were middle frontal gyrus (BA6 and 8), superior frontal gyrus (BA6) and anterior cingulate gyrus (BA24 and 32). Step 3, based on the comprehensive results of qualitative and quantitative research analyses, a spatio-temporal model of decreased distribution in brain regions of WMT was proposed, and there were 5 results and their discussions obtained with the said model. Step 4, 31 out of the 37 papers were analyzed using non-parametric tests to check the factors that possibly regulated the training effect of WMT, which showed that the respective effect of task type and time of the training on brain activation had statistically significant. Step 5, according to the above analyses, 3 conclusions were drawn regarding the spatio-temporal characteristics of the neuroplasticity induced by WMT in normal population, i.e. 1) WMT can either weaken or enhance the brain activities, and the weakening effect appears more prominent. In addition, updating and shorter time of WMT tend to induce more weakening effects; 2) Such changes occur mostly in the frontal-parietal association area, but also possibly in the temporal lobe, occipital lobe, cingulate gyrus, striatum and other association areas, which reflects the whole brain functional association within a certain range. It highlights the spatial characteristics of the neuroplasticity, which fits in easily with ExtPFIT; 3) The 4 sub-regions of the middle frontal gyrus, superior frontal gyrus, inferior parietal lobule and cingulate gyrus (anterior in particular) in decreased activation mainly reflect the spatial characteristics of the neuroplasticity which fits in easily with “neural efficiency hypothesis” and “smarter brains may be slightly lazier”. Step 6, it was proposed that further research of the neuroplasticity induced by WMT was to possibly focus on low brain activity on its plasticity, distinguish the temporal characteristics of the training effect reflected in 4 sub-regions of middle frontal gyrus, superior frontal gyrus, inferior parietal lobule and cingulate gyrus (anterior in particular), as well as to probe the comprehensive influencing factors that increase or decrease the brain activities.

Particle plugging in porous media under stress dependence by Monte Carlo simulations

Accurate prediction of particle plugging behavior in porous media under stress conditions is crucial for characterizing particle flow in porous media. Specifically, due to particle plugging in porous media, rock deforms and permeability changes in the particle-fluid system. Therefore, it's significant to study particle plugging behavior in porous media under stress conditions. In this work, in view of the fractal character of pore structure and Hertz theory, probability models for stress dependent pore size and particle plugging strength (i.e., drag force exerting on the unit area of the particle) are developed using Monte carlo simulations. Compared with the existing models, our derived model considers more factors, such as pore structural parameters, particle size, lithology parameters of particles and rocks, and effective stress. The reliability of the derived model has been significantly improved due to the consideration of the more comprehensive influencing factors. Several case studies have been performed to validate the accraucy of the developed model. Eventually, the derived model has been applied to analyze a real case in Changqing reservoir in China and the analysis results show that our derived model can provide the theoretical basis to design the reasonable quantity of profile control agent (preformed particle gel) in oilfield.

Crystal-Structure-Dependent Photocatalytic Redox Activity and Reaction Pathways over Ga2O3 Polymorphs.

Differentiated crystal structures generally affect the surface physicochemical properties of catalysts, causing variety in catalytic activity between polymorphs. However, the underlying mechanism has not been completely revealed, especially the influence of surface physicochemical properties on photocatalytic redox activity and the reaction mechanism. In this work, we reveal the mechanism of surface redox properties on different crystal forms of gallium oxide from a molecular level. α-Ga2O3 and β-Ga2O3 exhibit a slight difference in catalytic oxidation of organic pollutants due to comprehensive influencing factors, including their valence band position, reactive oxygen species, and pore structure properties related to the adsorption-reaction-desorption process. But the catalytic reduction ability of CO2 is obviously different due to the large differences of interaction between the surface of crystal structures and CO2 molecules, which are critical to determine the catalytic performance and reaction pathways. The enhanced adsorption and activation of CO2 on the α-Ga2O3 surface could promote the reduction reaction efficiency. Moreover, the large energy barrier of CH2* formation on β-Ga2O3 makes the formation of methane (CH4) relatively difficult compared to that on α-Ga2O3. The yield rate of CH4 (1.8 μmol·g-1·h-1) on α-Ga2O3 is three times better than that on β-Ga2O3 (CH4: 0.6 μmol·g-1·h-1). The current findings can offer novel insights into the understanding of crystal-structure-dependent photocatalytic performances and the design of new catalysts applied in energy conversion and environmental purification by crystal structure-tuning.

Correlation between surface roughness parameters and contact stress of gear

To reveal the relationship between rough tooth surface microstructure characteristics and contact performance, contact analysis is carried out based on the measured surface topography of the tooth surface of ultrasonic grinding by utilizing the elastic-plastic contact mechanics and statistical correlation analysis theory. Simple correlation analysis and partial correlation analysis are adopted for the gear three-dimensional surface roughness parameters and the maximum Mises stress and maximum shear stress. Then, the method of stepwise regression analysis and path analysis is used to construct the best fitting linear model of 3D roughness parameters and the maximum Mises stress and maximum shear stress, and the parameters’ decision coefficient is obtained. The research shows: (1) the rank of comprehensive influence factors of the maximum Mises stress is as follows: arithmetical mean height ( Sa), peak material portion ( Smr1), maximum peak height ( Sp), reduced peak height ( Spk), and minimum curvature radius and height ratio ( K), where Smr1, Spk, and K are negatively correlated with the maximum Mises stress; (2) the comprehensive influence variables of the maximum shear stress are in the order of Sa, Spk, and Vmp, among which Spk and Vmp are inversely related to the maximum shear stress.