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In the field of cement, if the formation cannot be given sufficient pressure to maintain stability during construction, pressure control failure may occur, leading to the leakage of liquids and gasses from the formation to the wellbore. In addition, irregular wellbore diameter and casing eccentricity are important factors that are easily overlooked and affect the prediction of ECD (Equivalent Circulation Density) calculation. This results in major accidents and ecological disasters, further impacting the global environment. This study focuses on a well in the eastern oilfields of China, and based on a rheological experiment of high temperature and high pressure, an irregular eccentric wellbore model is established according to the measured wellbore diameter and eccentricity data to calculate the ECD of the whole cementing process. Then, a data set is constructed and analyzed using the random forest method to quantitatively evaluate influencing factors such as displacement, rheology, density, and eccentricity on the bottomhole and wellbore ECD. Results find that the density of cement slurry and drilling fluid has the most significant impact on the maximum ECD, with the impact reaching 0.3142 and 0.2902, respectively, and the main factors that affect the minimum ECD are the density and rheological changes in the drilling fluid, reaching 0.7014 and 0.2846. These research findings will contribute to the precise control of wellbore pressure during cementing operations, further ensuring the safety of cementing operations, and laying a technical foundation for the automation and intelligentization of subsequent cementing operations. [ABSTRACT FROM AUTHOR]

Groundwater is abundant in soft soil area, and water inrush may occur during excavation of deep foundation pit. To keep the construction safety of the foundation pit, foundation pit dewatering is often conducted by combining waterproof curtains and dewatering wells, resulting in groundwater level drawdown outside the foundation pit (ΔHo). There are many factors influencing the change of groundwater level during dewatering which are summarized as four types, including the design parameters of the waterproof curtains, the design parameters of dewatering wells, characteristics of target dewatering aquifer, and other factors. A foundation pit engineering database was established by collecting 101 dewatering engineering cases in Shanghai, and nine influencing factors were selected for statistical analysis to obtain the influence weights of each influencing factor on ΔHo. The numerical standardized data statistical analysis method is used for the weight assessment and the nine groups of the data corresponding to the nine influencing factors were normalized by min–max standardization method based on linear model. The results indicate that water retaining depth ratio of the waterproof curtain (Rw), height difference between the filter tube and the waterproof curtain (Rp), vertical permeability coefficient (Kv), and length ratio of the filter tube (Rl) are the primary factors that should be prioritized in foundation pit dewatering design optimization in Shanghai. [ABSTRACT FROM AUTHOR]

The rapid discharge of cuttings from the air cushion chamber is crucial for the construction safety and excavation efficiency of the slurry shield, previous studies have mainly focused on the transport characteristics of cuttings in the slurry discharge pipe, while the complete process of cuttings entering the slurry discharge pipe from the air cushion chamber until its discharged is often overlooked. Based on the CFD-DEM (computational fluid dynamics-discrete element method) coupling calculation method and combined with the actual project, a numerical calculation model that can fully reflect the discharge of cuttings in the air cushion chamber was established. The discharged ratio of cuttings under different scouring pipes layouts (vertical distance h1, horizontal distance h2 and rotation angle β) was compared, and the sensitivity of different factors to the discharged ratio of cuttings was summarized. The results show that the discharged ratio of cuttings decreases with the increase of h1, and increases with the increase of h2 and β. The average sensitivity order of the three factors is h1 (1. 6) > h2 (0. 79) > β(0. 61) . Therefore, reducing the height of the scour pipes is the most effective measure to improve the cuttings discharged ratio in practical engineering. The research results can provide practical reference for optimizing the layout of scouring pipes and formulating relevant construction measures. [ABSTRACT FROM AUTHOR]

This study investigates the determinants influencing citizens' intentions to embrace and use e-government services in developing countries. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, known for their rigorous and systematic approach, are employed to examine the fundamental attributes, provide extensive descriptive statistics, synthesize the elements, use analytical tools, and present the results from the selected quantitative papers. A weighted meta-analysis was performed on forty-three quantitative research articles on e-government adoption, encompassing 401 relationships, and published in journals within the last ten years. All the participants are from the Asian and African continents. The findings suggest that perceived trust, perceived quality, performance expectancy, effort expectancy, social influence, self-efficacy, and facilitating conditions are the most crucial factors of behavioral intention via the mediation effects of attitude and perceived satisfaction. Furthermore, several demographic characteristics, such as age, gender, education, and experience with e-government services, moderate the association between the mediators and behavioral intention. Thus, we propose a new citizen-centric model named the Integrated Model of Egovernment Adoption (IMEGA), designed to address the current research gap and predict the extent to which citizens in developing countries would accept e-government services. This paper examines the implications of the findings for both theoretical frameworks and practical applications. Furthermore, the limits of the current study are acknowledged, and future research directions have been provided. [ABSTRACT FROM AUTHOR]

Based on panel data from 285 prefecture-level cities in China from 2019 to 2023, the synergistic effects of the digital economy, green technology innovation, and ecological environment quality were analyzed. First, using the entropy method, the measurement dimensions of the indicators of the digital economy, green technology innovation, and ecological environment quality were obtained. Second, employing a neural network model with these measurements as input variables, the interactive relationship among the digital economy, green technology innovation, and ecological environment quality was explored. Finally, based on the calculation results of the neural network model, the importance and impact of each input parameter on ecological environment quality were determined using weight analysis methods. The research findings indicate: (1) Utilizing the entropy method, the measurement dimensions of the indicators of the digital economy, green technology innovation, and ecological environment quality were obtained. Analysis of each indicator measurement reveals that environmental pressure has a significant impact on ecological environment quality, with significant differences in environmental pressure among different regions. Industrial digitization emerges as the core factor influencing the digital economy, being the most significant driving effect, followed by digital industrialization. Green technology innovation is crucial for promoting environmental protection and achieving high-quality green economic development. (2) Based on the neural network model, the interactive relationship among the digital economy, green technology innovation, and ecological environment quality was revealed. The results indicate that the digital economy has a direct impact on improving ecological environment quality. The relationship between the digital economy and the ecological environment exhibits nonlinear effects, with the rate of change in environmental pressure and environmental status measurements initially increasing significantly and then gradually slowing down as the measurement levels of digital industrialization and industrial digitization increase. Improvement in digital governance and data value measurement levels will contribute to enhancing environmental status and environmental governance levels. (3) Through weight analysis, it was found that in terms of direct effects, industrial digitization, and digital industrialization have the most significant impact on environmental pressure, with importance coefficients of 0.45 and 0.3, respectively, while data valorization has the least impact. Regarding intermediary effects, industrial digitization and green technology innovation have the most significant impact on environmental pressure, while digital governance and green technology innovation have a relatively clear impact on environmental status and environmental governance. These results lay the foundation for promoting the coordinated cooperation between the digital economy and green technology innovation and for advancing the establishment of a win–win situation between economic development and environmental protection. [ABSTRACT FROM AUTHOR]

Given their strong potential to mitigate environmental problems and contribute to the decarbonization of energy systems, researchers and practitioners are demonstrating increasing interest in local energy communities. Accordingly, the current study presents a meta and weight analysis that combines the quantitative results of previous research on the topic of citizen participation in these communities, revealing the main tendencies, theories, and constructs evident in the field. Through an analysis of 24 articles, the results identify the theory of planned behavior as the leading ground theory, together with important dimensions such as trust, personal characteristics, return on investment, and pro-environmental and altruistic factors. The findings of this work can support future researchers and practitioners in developing strategies to increase the creation of local energy communities.

In the field of cement, if the formation cannot be given sufficient pressure to maintain stability during construction, pressure control failure may occur, leading to the leakage of liquids and gasses from the formation to the wellbore. In addition, irregular wellbore diameter and casing eccentricity are important factors that are easily overlooked and affect the prediction of ECD (Equivalent Circulation Density) calculation. This results in major accidents and ecological disasters, further impacting the global environment. This study focuses on a well in the eastern oilfields of China, and based on a rheological experiment of high temperature and high pressure, an irregular eccentric wellbore model is established according to the measured wellbore diameter and eccentricity data to calculate the ECD of the whole cementing process. Then, a data set is constructed and analyzed using the random forest method to quantitatively evaluate influencing factors such as displacement, rheology, density, and eccentricity on the bottomhole and wellbore ECD. Results find that the density of cement slurry and drilling fluid has the most significant impact on the maximum ECD, with the impact reaching 0.3142 and 0.2902, respectively, and the main factors that affect the minimum ECD are the density and rheological changes in the drilling fluid, reaching 0.7014 and 0.2846. These research findings will contribute to the precise control of wellbore pressure during cementing operations, further ensuring the safety of cementing operations, and laying a technical foundation for the automation and intelligentization of subsequent cementing operations.

Geological disasters occur frequently in hilly areas of Sichuan province, and the development of rural settlement production and life may be affected by geological disasters.This study aims to assess the spatial vulnerability of rural settlements in hilly areas of Sichuan Province through Back-propagation neural network, find the main factors that really affect the spatial vulnerability of rural settlements, analyze the distribution characteristics of spatial vulnerability of rural settlements in hilly areas of Sichuan Province, and find methods to reduce the spatial vulnerability of rural settlements. Through research, it can be found that the three main factors affecting the spatial vulnerability of rural settlements in hilly areas of Sichuan are climate factors, soil erosion degree, and geological hazard hidden danger degree. The quantitative climate factor data is the ratio of annual precipitation to population in rural settlements,the higher the annual precipitation, the higher the spatial vulnerability. The second largest influencing factor is the degree of soil erosion,the more severe the soil erosion, the higher the spatial vulnerability in the districts and counties. The third major influencing factor is the degree of geological hazards, and the higher the degree of geological hazards, the higher the spatial vulnerability. Other natural environment and rural settlement spatial factors also have a certain impact on the spatial vulnerability of rural settlements, while socio-economic factors have almost no impact on the spatial vulnerability of rural settlements.Finally, the relationship between the spatial vulnerability of rural settlements and its location are obtained from the relevant data of all sample districts and counties, and combined with the geological environment and climate of the hilly areas of Sichuan, the spatial distribution characteristics of the spatial vulnerability of rural settlements in the hilly areas of Sichuan are summarized: The closer the districts and counties are to the geological fracture zone between Longmen Mountain and Longquan Mountain, the higher the spatial vulnerability of rural settlements.The spatial vulnerability of rural settlements in the west of hilly areas is higher than that in the east, and there is little difference between north and south. Then, based on the above achievements, this study proposes strategies and suggestions to reduce the spatial vulnerability of rural settlements in hilly areas of Sichuan, providing reference materials and data support for local planning departments in rural planning work.

We examine the effects of different process parameter settings on variations in melt-filling pressure, viscosity index, and part weight based on the use of real-time pressure sensor readings of the inside of the injection melt flow path. In the experiment, widely used polypropylene materials and round-shaped molded parts are selected as the molding and study object. At the same time, pressure sensors are used at different positions to perform data acquisition to enable research into the variation process of the melt-filling pressure, allowing for a viscosity index equation to simultaneously calculate viscosity indexes and observe weight variations in the samples. The obtained data will be a basis for setting up smart manufacturing in the future. The research showed the following: (i) the installation of the real-time pressure sensing modules allowed the variation process of the melt-filling pressure to be monitored along a path from the injection barrel to the mold cavity; (ii) the viscosity index was subject to changing the melt temperature, the injection speed, and the V/P switch-over point of the screw; and (iii) the melt temperature change had a considerable impact on part weight and changes in the injection speed had a relatively significant impact on viscosity variation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Academic Library & Information Science is the property of Anhui University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Railway Construction Technology is the property of Railway Construction Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Determining accurately the relative importance of evaluation indicators is one of the key technologies for lightning disaster risk zoning. In order to solve the problem of analytic hierarchy process (AHP) in judging the relative importance of indices by human experience, we proposed an improved AHP method (IAHP). In addition, using the lightning monitoring data from 2007 to 2020, digital terrain elevation data, soil conductivity data, as well as economic and social data of Hubei, we selected seven parameters: lightning strike density, lightning intensity, altitude, terrain undulation, soil conductivity, population density, and GDP density, as the risk evaluation indices of lightning disaster in Hubei Province, and then established a lightning disaster risk assessment model. Based on this model, we conducted a risk zoning of lightning disasters in Hubei province. The results indicate that the lightning disaster risk in Hubei Province can be divided into five levels, i.e., higher risk, sub high risk, medium risk, inferior risk, and lower risk. The higher and sub high risk areas mainly locate in the plains and low mountain and hilly terrain between the south of Dabie Mountain and the north of Mufu Mountain in eastern Hubei, as well as the most of Jianghan Plain and the transition zone from the western Hubei mountainous area to the Jianghan Plain. The inferior and lower risk areas mainly locate in Enshi, Shennongjia, Shiyan, western Yichang, and southwestern Xiangyang. In other areas of Hubei, most of them are mainly medium risk of lightning disaster. The lightning disaster risk index of each district or county and the lightning disaster frequency show good positive correlation, which reflects basically the potential risk of lightning disaster in Hubei Province.

Geopolymer cementitious materials can replace cement-based cementitious materials as curing agents in engineering problems, such as backfilling of narrow fertilizer troughs, and effectively reduce pollution and energy consumption in the cement production process. There are few studies on cementitious materials. Three new green cementitious materials combined with alkali activators are used to solidify engineering slag and form fluidized geopolymer-solidified soil. The strength prediction model is established to analyze the influence of different factors on the strength. The results show that the strength of the solidified soil increases first and then decreases with the increasing modulus of the alkali activator, increases with the content of GGBS, fly ash and rice husk ash, and decreases with the increasing particle size. When the modulus of alkali activator increases to 1.2, the content of GGBS increases to 10%, the content of fly ash increases to 8%, and the content of rice husk ash increases to 11%, the strength of the solidified soil increases significantly. The average relative error of the prediction results of the strength prediction model is only 5.57%, which is relatively accurate for the solidified soil. The calculation results of the weights of each layer in the prediction model show that the curing age has the greatest impact on the strength of the solidified soil, and the particle size of rice husk ash has the minimal impact. The research results can provide theoretical support for the application of solidified soil in practical engineering.

The virtual power plant (VPP) aggregates distributed energy resources (DERs) from the distribution network,which not only improves the response capability of aggregated resources,but also participates in electrical market to improve resource schedulability and obtain higher operation profits. The recent studies about the evaluation of VPP are not comprehensive,and there is a lack of analysis on the index weight for the VPP. In this paper,firstly,the evaluation model of response capability of the VPP is built with considering the response time,duration and so on. Then,a method for evaluating the overall responsiveness of VPP is proposed. Taking the independent responsiveness of distributed resources as a constraint,the multi-dimensional quantitative indicators of responsiveness are optimized to obtain the overall responsiveness of aggregated resources. At the same time,considering the instability of aggregated responsiveness,the indicator weight is calculated and the responsiveness is scored. Finally,the response capability curve and score of VPP within 24 hours are obtained through an example,and the effectiveness of the method in this paper is verified through analysis. The overall response capability information calculated by the method in this paper can be reported as the basis for power grid regulation,and the response capability score obtained can intuitively evaluate the aggregate response capability,which is helpful for the analysis and control of VPP response capability.

Rock mass classification is an essential basis for evaluating the stability of surrounding rock in tunnels and for determining reasonable construction methods and supporting parameters. The classification of surrounding rock mass in tunnels has a scale effect and advance demand. The grade of surrounding rock needs to be predicted in advance for different tunnel scales, so as to allow reaction time for support design optimization. Due to the concealment and uncertainty of parameter acquisition, the preliminary classification of surrounding rock in the tunnel exploration is quite different from the actual surrounding rock grade. This paper proposes a fine classification of surrounding rock in tunnel construction. Multiple geological methods such as geological mapping, ground-penetrating radar, tunnel seismic prediction, deepened blast holes, and advanced horizontal drilling are used to obtain the engineering geological response parameters and information relating to the surrounding rock in front of tunnel faces. Based on the sample database of surrounding rock in excavated sections, the influencing factors are analyzed and weighed. Finally, a prediction model for classifying the surrounding rock mass in unexcavated tunnel sections is established using extension theory and validated using actual engineering. Through the correction of the surrounding rock mass grades, the support optimization of the corresponding unexcavated tunnel sections has been completed, and the stability of the tunnel construction has been maintained. This study effectively improves the accuracy and engineering practicality of surrounding rock mass classification for tunnels and provides practical guidance for tunnel construction optimization. [ABSTRACT FROM AUTHOR]

The expansion of financial markets has enabled individuals to invest in a variety of securities and financial instruments. Consequently, behavioral finance has shed light on the characteristics and psychological processes that influence the investment intentions and decisions of investors. We performed a systematic review of the recent literature on the key elements that influence the behavioral intentions and investment decisions of individual investors. In combination with bibliometric and weight analysis, this review aims to propose a comprehensive approach to present quantitative and qualitative analyses of the rising elements influencing investors' intentions and behaviors in financial investment products. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, this work comprises a review of 28 articles published in Web of Science and Scopus databases between 2016 and 2021. The findings identify six underlying themes of investor behavior determined using content: (1) personal factors, (2) social factors, (3) market information, (4) firm-specific factors, (5) product-related factors, and (6) demography. The future research agenda is highlighted based on the Theories, Constructs, Contexts, and Methods framework. The findings provide insights for both theoretical and practical application for corporations, financial institutions, and policy makers in understanding investors' behavior so as to strengthen the financial industry and economy. [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology & Engineering Geology / Shuiwendizhi Gongchengdizhi is the property of Hydrogeology & Engineering Geology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Petroleum Geology & Oilfield Development in Daqing is the property of Editorial Department of Petroleum Geology & Oilfield Development in Daqing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: To study the contents of functional components and antioxidant capacity of different varieties of Indocalamus leaves in same area. Methods: Using 11 existing species of Indocalamus leaves resources as raw materials, the contents of basic nutrients, flavonoids, polyphenols, polysaccharides, volatile oil and other functional components were evaluated as well as the antioxidant capacity of alcohol extracts. Then the data were analyzed by weight and scored comprehensively. Results: The results showed that there were significant differences among the content of basic nutrients in leaves of different Indocalamus varieties in the same area. The coefficient of variation of four components was 13.93% only for crude protein, and the coefficient of variation among the other three was no more than 10%. Same phenomenon was found in the four functional components as well. The variation coefficients of flavonoids and polyphenols were calculated for 34.47% and 38.04%, 28.03% and 22.36% were for that of polysaccharide and volatile oil contents, in which the difference was smaller than that of flavonoids and polyphenols. Among them, the contents of flavonoids (0.68%) and polyphenols (0.62%) in the leaves of Indocalamus elongatus were the highest. Polysaccharide (0.69%) in leaves of Indocalamus barbatus was the highest. And the content of volatile oil (0.64%) in broad-leaved Indocalamus broad-leaved was the highest. The antioxidant capacity assay indicated that there was indistinctively difference in antioxidant activity among those Indocalamus leaves, and the coefficient of variation among the scavenging rates of DPPH radical, superoxide anion and hydroxyl radical was less than 10%. From the weight analysis containing the content of four functional components and antioxidant activity, the comprehensive score of Indocalamus Tianmu was the highest (0.8108), which might be used as the preferred material for the development and utilization of Indocalamus leaves. From the above studies, this finding would provide an important information for the utilization of Indocalamus leaves.

We examine the effects of different process parameter settings on variations in melt-filling pressure, viscosity index, and part weight based on the use of real-time pressure sensor readings of the inside of the injection melt flow path. In the experiment, widely used polypropylene materials and round-shaped molded parts are selected as the molding and study object. At the same time, pressure sensors are used at different positions to perform data acquisition to enable research into the variation process of the melt-filling pressure, allowing for a viscosity index equation to simultaneously calculate viscosity indexes and observe weight variations in the samples. The obtained data will be a basis for setting up smart manufacturing in the future. The research showed the following: (i) the installation of the real-time pressure sensing modules allowed the variation process of the melt-filling pressure to be monitored along a path from the injection barrel to the mold cavity; (ii) the viscosity index was subject to changing the melt temperature, the injection speed, and the V/P switch-over point of the screw; and (iii) the melt temperature change had a considerable impact on part weight and changes in the injection speed had a relatively significant impact on viscosity variation.

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Concrete materials are widely used in tunnel engineering. In China, the cold regions have gradually become the main area for highway and railway construction. Affected by high altitude, low temperature, turbulent wind, and other conditions, freezing damage, such as tunnel icing, occurs in concrete materials, which seriously affects the quality and operational safety of tunnels in cold regions. Therefore, it is necessary to carry out a quantitative analysis of various factors affecting freezing damage to protect concrete materials in tunnels. This paper summarizes various freezing damage phenomena in tunnels in cold regions and divides them into three types: water seepage and hanging ice type freezing damage, lining interface type freezing damage, and tunnel foundation ice accumulation type freezing damage. Based on the qualitative evaluation of each factor, the affiliation of each factor was divided. Then, the influence weight of each factor on freezing damage was obtained through the analytic hierarchy process, and then each factor was ranked. This study is helpful to the selection of anti-freezing measures for tunnels in cold regions. [ABSTRACT FROM AUTHOR]

The research in the literature review on Internet of Things (IoT) adoption from an individual consumer viewpoint is minimal and has not yet been fully investigated. Therefore, the objectives of this study are to analyze the growth of IoT in recent years and to conduct a weight analysis of the factors that affect acceptance intentions and real usage of IoT-enabled services. For the review, we analyzed 87 publications from 13 conferences and 54 journals published during the period 2014-2020 about consumer adoption of IoT. Following the study, we discovered an unprecedented increase in the number of articles published in the last seven years, which points to an emerging area with an enormous prospect. Furthermore, the weight analysis outcome was associated with the diagrammatic representation in this study. After that, this research developed a generalized consumer IoT adoption model based on the 12 best predictors derived from frequency count and weight analysis, which had the highest predictive power for calculating IoT adoption. This paper further acknowledges the study's theoretical and practical contributions, as well as its shortcomings, and proposes further research directions for future researchers. [ABSTRACT FROM AUTHOR]

The research in the literature review on Internet of Things (IoT) adoption from an individual consumer viewpoint is minimal and has not yet been fully investigated. Therefore, the objectives of this study are to analyze the growth of IoT in recent years and to conduct a weight analysis of the factors that affect acceptance intentions and real usage of IoT-enabled services. For the review, we analyzed 87 publications from 13 conferences and 54 journals published during the period 2014-2020 about consumer adoption of IoT. Following the study, we discovered an unprecedented increase in the number of articles published in the last seven years, which points to an emerging area with an enormous prospect. Furthermore, the weight analysis outcome was associated with the diagrammatic representation in this study. After that, this research developed a generalized consumer IoT adoption model based on the 12 best predictors derived from frequency count and weight analysis, which had the highest predictive power for calculating IoT adoption. This paper further acknowledges the study’s theoretical and practical contributions, as well as its shortcomings, and proposes further research directions for future researchers.

By using FLAC3D software, the slope numerical model of anchorage open-pit mine is established, and the influence of anchorage parameters on dynamic response of bolt, displacement response of slope and plastic strain through length of slope is studied, and the sensitivity of anchorage parameters is analyzed by weight analysis method. The results show that with the increase of bolt length and anchor hole diameter, the permanent horizontal displacement of slope and the penetration rate of plastic strain zone decrease, and the axial force peak of bolt increases gradually. With the increase of vertical spacing of bolt, all three increase. With the increase of anchoring inclination angle, the permanent horizontal displacement of slope and the plastic strain area first decrease and then increase, and the peak axial force of bolt first increases and then decreases. The weight of anchorage parameters on bolt axial force is in order from high to low: anchor hole diameter, bolt length, bolt spacing, anchor inclination angle. The weight of influence on penetration rate of plastic strain zone is the bolt spacing, anchor hole diameter, bolt length and anchor inclination angle successively from high to low. The weight of influence on slope displacement from high to low is bolt spacing, bolt length, anchor hole diameter and anchor inclination angle.

With the development of society, big data's concept is mentioned more and more. However, in the numerous and complex mass data, the effective information is often very limited, and the data often show the complex characteristics of "close and orderly short-term correlation and large long-term disturbance", which is difficult to be expressed by a long sequence composed of a single real value. This paper takes the interval grey number as the research object for the uncertain system of "less data and poor information". According to the multi-attribute group decisions problem with attribute’s values of interval grey number and the weights of decision-makers and attributes that are completely unknown, a method of group decision making with interval grey numbers based on grey correlation and relative close degree is proposed. For the purpose of making effective decisions, the weights of decision-makers and attributes are calculated by correlation degree of decision information which is measured by the model of grey correlation analysis based on interval grey number deviation degree. On this basis, the correlation degree between the scheme and the positive and negative ideal schemes are computed, and then the grey correlation and relative close degree of the scheme is calculated. Finally, the scheme is sorted according to the value of the grey correlation and relative close degree. The rationality and feasibility of the proposed method are verified by numerical examples.

As a common urban flood control facility, mobile flood control wall is widely used in different cities. In order to provide design basis for the initial parameters of mobile flood control wall, this paper analyzed the structural influence of mobile flood control wall by finite element analysis and determined the analysis parameters of Response Surface Method as center column height, center column width and baffle span length. The response data of design test group were calculated by finite element method, which provided sufficient sample data for Response Surface Method analysis. Then, based on the Response Surface Method, the parameters obtained from finite element analysis were analyzed and a reasonable experimental control group was designed. According to the finite element data and actual cost data, the parameter weights of stress value and displacement value of flood control wall were analyzed and the fitting curves of corresponding parameters were calculated to obtain the comparison results of residual value, predicted value and actual value and the response surface diagram. Then, according to the actual project and cost, the optimal size of flood control wall design was obtained by analysis, which provided a reliable scheme for the design and structural optimization of mobile flood control wall. [ABSTRACT FROM AUTHOR]

Advancements of information and communication technologies (ICT) have made it possible for governments to shift a vast majority of their services online, creating what is known as electronic government (e-gov). Many researchers have been studying the factors influencing users' behavioural intentions to use such services. This study methodically examines and visualizes the various relationships between variables and their overall performance by reviewing the findings of 51 publications in the context of the Arab gulf countries (known as GCC countries). The study provides a holistic diagrammatic representation of the synthesized models depicting the various types and directions of relationships between variables, followed by a weight analysis of the frequently used relationships in order to determine their overall cumulative performance. The outcomes of this review contribute to theory and practice by identifying e-gov adoption research patterns and gaps and by providing recommendations to policymakers and officials involved in developing and implementing e-gov systems. [ABSTRACT FROM AUTHOR]

To explore the trend of fatty acid methyl esters (FAMEs) during the oxidation process of biodiesel, this study uses gas chromatography - mass spectrometry (GC-MS) combined with information entropy theory to propose a new method to analyze the effect of FAMEs on the stability of biodiesel (especially with different amounts of unsaturated FAMEs). The induction periods of the main FAMEs in biodiesel were determined by the Rancimat method. The results showed that the longest induction period of methyl stearate was 248 h, while the induction of methyl linoleate was only 0.12 h. Accelerated oxidation of biodiesel, using GC-MS analysis showed that the oxidation trends of FAMEs was different. The oxidation rate of methyl linoleate was the fastest. A mathematical equation was proposed to calculate the trend of FAMEs. The results showed that during the 5 h accelerated an oxidation process, the content of methyl linoleate decreased from 28.35% to 2.10%, while the content of methyl oleate decreased by 18.21%. Using information entropy, the weighting coefficients of FAMEs in the oxidation process of biodiesel were calculated. The weighting coefficient of methyl linoleate was as high as 0.6797. Methyl linoleate was the main reason why biodiesel was easily oxidized. • GC-MS and information entropy methods was employed for FAMEs oxidation evaluation. •The weight coefficients of different FAMEs in the oxidation process are obtained. •Methyl linoleate is the main internal reason that affects the oxidation of biodiesel. [ABSTRACT FROM AUTHOR]

Taiwan is located in a hot and humid subtropical climate. Urban development, building heat emissions, and human activities lead to the urban heat island (UHI) effect in which urban air temperatures are significantly higher than those in the surrounding suburban areas. In this study, the factors contributing to the UHI effect were investigated in a case study of Taichung City, Taiwan by using high-resolution geographic climate data. Geographic Information System tools were used for grid-based spatial data analysis, and a physiological equivalent temperature index map was calculated and overlaid to identify heat zones. The results revealed that environmental factors have complex, nonlinear effects on temperatures in in urban areas; hence, the analysis was challenging. The decision tree algorithm was used to identified key environmental factors contributing to urban heat in areas with different levels of development; a weighting analysis was then performed to select effective urban cooling strategies. Finally, backpropagation neural network models tailored to different development levels were used for simulations to validate the proposed cooling policies. The results of this study were incorporated into the urban planning policies of Taichung City, Taiwan to mitigate the UHI effect and create a more suitable urban living environment. • High-res data reveal factors like urban development causing the UHI effect. • Study uncovers intricate, nonlinear relationships among factors affecting UHI. • Decision tree algorithm identifies key factors and cooling strategy selection. • Neural network validate proposed policies, influencing planning to mitigate UHI. [ABSTRACT FROM AUTHOR]

Using the cloud-ground lightning locating data from 2007 to 2017 and the thunderstorm day data from 1961 to 2003 in Hubei, we selected six lightning parameters such as thunderstorm day, cloud-ground lightning density, lightning current wave-head gradients, lightning current amplitude, large current density and small current density as risk evaluation indices of lightning disaster in Hubei province, and then combined them with the projection pursuit method to establish a lightning disaster risk assessment model. Based on this model, we conducted a risk zoning of lightning disasters in Hubei province. The results indicate that the lightning hazards in Hubei province can be divided into five risk levels, i.e., higher risk, sub high risk, medium risk, inferior risk and lower risk. The lightning disaster risk is higher in the east than that in the west, which gradually decreases from east to west. The higher and sub high risk areas mainly locate in the eastern regions of Hongan, Xiaochang, Xiaonan, Dongxihu, Caidian, Hannan and Honghu, while the inferior and lower risk areas mainly distribute in the western regions of Xiangzhou, Xiangcheng, Gucheng, Baokang, Xingshan, Zigui, Badong and Wufeng. The zone between the eastern and western regions mentioned above is mainly a medium-risk zone with a total of 27 districts or counties or cities. The results of lightning disaster risk zoning can provide scientific basis for local lightning disaster prevention and mitigation work for effective reduction of disaster losses.

There always exist contrast reduction and details degradation when the infrared imaging system works on water surface because of fog and water mist. An image enhancement method is proposed based on radiative transfer theory. Firstly, the infrared enhancement model is built according to radiative transfer theory, with which we construct an optimization frame. Secondly, edge matrix is designed for weight analysis, which is particularly important for next estimation of fine transmissivity and optical path radiation. Finally, enhancement is achieved with edge matrix. The main contribution and novelty includes the derivation of enhancement based on radiative transfer theory, design of edge matrix, and estimation of fine transmissivity and optical path radiation. The experimental comparisons show the proposed method can generate results with a good visual effect, which is proved by both subjective and objective assessment. The experiments also indicate that the proposed algorithm runs fast to apply in real system.

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The results are presented from a study of three-layer geodesic dome structures with bar fillers under their own weight. An algorithm was developed for selecting the type of structural layout used and the reference parameters chosen in terms of the technological, strength, and weight characteristics. The results of this analysis aim to make it easier for designers to determine the optimal reference parameters in the initial stage of the designing of geodetic hemispherical dome structures, the construction of which is planned to be carried out in remote areas with harsh climatic conditions. Due to the lack of sufficient ground transport infrastructure, cargo delivery to these regions is currently possible only with the help of air transport. The importance of this study rests on the lack of adequate methods for the determination of the reference parameters for geodesic hemispherical dome structures at an early stage of design. In particular, it is common for the issues regarding the transportation of structural elements as well as those that involve ensuring the strength and the technological characteristics of the structure to not be considered simultaneously. This study owes its relevance to the rapid development of the uninhabited territories of the Russian Federation in the context of the global ecological crisis caused by anthropogenic impact on the environment.

Traffic sign recognition is meaningful for real-world applications such as self-sufficient driving, traffic surveillance, and driver safety. However, traffic sign recognition is a hard problem because different sizes, illuminations, and noises affect the sign detection and recognition. This work recognizes Taiwan's prohibitory signs using deep learning methods. First, we develop a traffic sign database since there is no such kind of database available in Taiwan. Next, we adopt three different You Only Look Once (Yolo) networks (Yolo A, Yolo B, and Yolo C) and three various Yolo V3 SPP networks (Yolo D, Yolo E, and Yolo F) for prohibitory sign recognition. Finally, we conduct the comparative experiment of Yolo V3 and Yolo V3 SPP with different weights provided by the darknet framework (the best weight, the final weight, and the last weight). Experimental results show that the mean average precision (mAP) observation of all models that the Yolo V3 SPP is better than other models. Yolo D took the optimum average accuracy at 99.0%, followed by Yolo E and Yolo F 98.9%. The accuracy of Yolo V3 SPP is growing within the detection time, but it needs more time to identify the sign. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shenzhen University Science & Engineering is the property of Editorial Department of Journal of Shenzhen University Science & Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The aim of this research is to synthesise findings from previous studies by employing weight and meta-analysis to reconcile conflicting evidence and draw a "big picture" of eWOM factors influencing consumers' intention to buy. By using the findings from 69 studies, this research identified best (e.g. argument quality, valence, eWOM usefulness, trust in message), promising (e.g. eWOM credibility, emotional trust, attitude towards website) and least effective (e.g. volume, existing eWOM, source credibility) predictors of intention to buy in eWOM research. Additionally, the effect size of each predictor was calculated by performing meta-analysis. For academics, understanding what influences consumers' intention to buy will help set the agenda for future research directions; for practitioners, it will provide benefit in terms of practical guidance based on detailed analysis of specific factors influencing consumers' intention to buy, which could enhance their marketing activities. [ABSTRACT FROM AUTHOR]