Search results

Hydraulic conductivity is very low in saline-sodic soil, which decreases water infiltration. For saline-sodic soil, increasing infiltration water has a special meaning. Increasing infiltration water not only increases the water in the soil profile but also decreases the salinity of the soil, thus making it suitable for growing crops. This study aims to examine the effect of sand pipes on soil water and salt distribution through laboratory tests with different depths and diameters of sand pipes.

The soil water and salt distribution responses to different sand pipe depths and diameters was investigated. Treatments included sand pipes with diameters of 4 cm, 5cm, 6 cm and the same depth of 4 cm; with depths of 2cm, 6 cm and the same diameter of 5 cm, and a control with no sand pipe (with the diameter of 0 cm and the depth of 0 cm).

The results suggested that the amount of cumulative infiltration water and transport distance of the wetting front could be increased by increasing the depth and diameter of sand pipes. The soil water content in the soil profile decreased under all treatments except for the control, whereas the value of EC increased with increasing distance from the film hole center. Positive relationships were also found among the sand pipe depth, diameter and the zone of low salt content. Furthermore, salt leaching depth increased with sand pipe depth and diameter. Overall, the treatments with and without sand pipes exhibited obvious differences.

The correlation analysis proved that increasing the infiltration area through sand pipes positively affected the amount of infiltration water, wetting scope and salt leaching depth.

This study aims to analyze the changes in the ecosystem service value (ESV) in response to land use and contribute significantly to ecological construction and sustainable development.

The present study was conducted in the upper Zhanghe River region based on the moderate-resolution imaging spectroradiometer Land Cover Type product MCD12Q1 by using geographic information system (GIS) methods to process and re-classify the land-use data and using the Chinese ESV equivalent weight factors to investigate changes in land use and resulting changes in ESV between 2001 and 2013.

The results showed significant fluctuations in ESV between 2001 and 2013: there was a decline in ESV from 2001 to 2004, followed by a gradual rise after 2004, and the overall ESV exceeded 2001 levels by the end of 2013. However, the pattern of ESV change differed across geographic locations, and each administrative region contributed differently to the overall trend. The analysis confirmed that the land-use change was closely related to the change in its ESV, and the coefficients of sensitivity of ESV for all types of land use were less than one, indicating that the coefficient value of ESV lacked elasticity.

Therefore, to promote sustainable development in the upper Zhanghe River region, ESV should be taken into consideration when planning land use, especially for land types with high ESV, such as water bodies and forestlands.

The results can provide scientific support for the sustainable development of the ecological, economic and societal aspects of the upper Zhanghe River region. In addition, county-level administrative divisions were set as the basic research unit for the analysis and discussion of ESV changes in each unit within the research period and its impact on the overall ESV of the entire area to lay down a foundation for the analysis of the ESV spatial dynamic distribution in the entire research site.

The remote sensing data have become the irreplaceable source of data for the regions with little or without rainfall data, but these data also require scientific analysis, correction and application. This paper uses FY-2 rainfall data and the case studies of the droughts occurred in the Weihe River Basin from 2006 to 2009 to monitor the spatial and temporal evolution of climatic droughts. The monitoring results indicate that: (1) Except for 2008 which was a dry year, the other years in the Weihe River Basin had normal dry/wet conditions; (2) From October 2008 to January 2009, the rainfall was significantly reduced across the Weihe River Basin, and the continual rainfall was even less than 1 mm for December and January with a precipitation anomaly percentage lower than -80%, a sign of severe climatic drought. But the rainfall has improved since February 2009, when the precipitation reached 17.8 mm and Pa exceeded 100%, which helped to relieve the stress from drought resistance. A heavy precipitation continued for four months from June to September 2008, with the Pa exceeding 50%; (3) Due to the better temporal and spatial continuity than the ground-based meteorological observation, FY-2 precipitation data have good application prospects in the meteorological drought monitoring at a national or regional macro-scale.

The product family assembly line (PFAL) is a mixed model-assembly line, which is widely used in mass customization and intelligent manufacturing. The purpose of this paper is to study the problem of PFAL, a flexible (evolution) planning method to respond to product evolution for PFAL, to focus on product data analysis and evolution planning method.

The evolution balancing model for PFAL is established and an improved NSGA_II (INSGA_II) is proposed. From the perspective of data analysis, dynamic characteristics of PFAL are researched and analyzed. Especially the tasks, which stability is considered, can be divided into a platform and individual task. In INSGA_II algorithm, a new density selection and a decoding method based on sorting algorithms are proposed to compensate for the lack of traditional algorithms.

The effectiveness and feasibility of the method are validated by an example of PFAL evolution planning for a family of similar mechanical products. The optimized efficiency is significantly improved using INSGA_II proposed in this paper and the evolution planning model proposed has a stronger ability to respond to product evolution, which maximizes business performance over an effective period of time.

The assembly line designers and managers in discrete manufacturing companies can obtain an optimal solution for PFAL planning through the evolution planning model and INSGA-II proposed in this paper. Then, this planning model and optimization method have been successfully applied in the production of small wheel loaders.

The purpose of this paper is to find an accurate, efficient and easy-to-implement point estimate method (PEM) for the statistical moments of random systems.

First, by the theoretical and numerical analysis, the approximate reference variables for the frequently used nine types of random variables are obtained; then by combining with the dimension-reduction method (DRM), a new method which consists of four sub-methods is proposed; and finally, several examples are investigated to verify the characteristics of the proposed method.

Two types of reference variables for the frequently used nine types of variables are proposed, and four sub-methods for estimating the moments of responses are presented by combining with the univariate and bivariate DRM.

In this paper, the number of nodes of one-dimensional integrals is determined subjectively and empirically; therefore, determining the number of nodes rationally is still a challenge.

Through the linear transformation, the optimal reference variables of random variables are presented, and a PEM based on the linear transformation is proposed which is efficient and easy to implement. By the numerical method, the quasi-optimal reference variables are given, which is the basis of the proposed PEM based on the quasi-optimal reference variables, together with high efficiency and ease of implementation.

This study investigates the mediating role of service recovery between brand love (BL), brand trust (BT) and purchase intention in the context of food-delivery apps.

This study follows a quantitative approach. The authors conducted an online survey and collected 275 responses from users of food-delivery apps in South America and Asia. The authors analyzed the conceptual model proposed using structural equation modeling (SEM) in Smart PLS 3.0.

The results showed a direct and significant relationship between brand love, BT and purchase intention. Additionally, the authors identified the mediating role of service recovery between brand love, BT and purchase intention.

First, this study focused on the service recovery construct in general. Future research can address different types of service recovery, for example, core, interpersonal and procedure failures (Kim and Jang, 2016). Second, the authors restricted the study to the relationship between brand love, BT and purchase intention. Future studies can include other constructs, such as e-word of mouth, loyalty and information risk, as intervening variables. A larger sample can also be considered to support the generalization of the findings.

This study recommends that companies enchant customers with immediate actions after a service failure has occurred. In doing so, companies must monitor those customers who have experienced a service failure, measuring the level of trust in the branded app and checking the frequency of purchases after a service recovery. Interacting with customers through messages is also an important action to manage their purchase intention following the problem's solution. Furthermore, companies must segment customers who have experienced a failure and direct them to specific benefits to reinforce their trust in the app. Then, after correcting the problem, they should pay them special attention by offering benefits, like discounts, coupons and free delivery, as a strategy to promote future purchases.

This is the first paper to investigate the impact of service recovery on brand love, BT and purchase intention in the context of food-delivery services. The authors extend the knowledge about consumers' responses in the case of a failure caused by consumers' loved brands and show how service recovery actions can establish BT and influence future purchases.

Safety management system (SMS) has been widely adopted to explore its influence on safety performance (SP). However, most existing researches recognized SMS as a one-dimension structure and neglected the influences of its subdimensions. Similarly, the impact of safety responsibility (SR) on SP received little attention. This study aims to explore the relationship between subdimensions of SMS and SP, while incorporating the mediating effect of SR.

The research data were gathered from safety management evaluation report of a large real estate enterprise in China during 2010–2017. This paper carries out a series of data analyses to explore the impact of SMS and SR on SP. In order to analyze the synergistic impacts of SMS and SR on SP, path analysis, correlation analysis and mediation analysis were conducted using hypotheses concerning with the main subdimensions.

The results indicated SMS and SR decreased the project risk level and improved SP of real estate projects. Furthermore, the effect of SR partially mediated the relationship between the SMS and SP.

Findings in this research contribute to improve SP in real estate industry as well as other industries by the active assumption of SR and the successful implementation of SMS.

This research shows the relationship between subdimensions of SMS and SP and the mediating role of SR on SMS–SP relationship to improve SP in real estate industry.

The purpose of this paper is to improve the online monitoring level of low-frequency oscillation in the power system. A modal identification method of discrete Fourier transform (DFT) curve fitting based on ambient data is proposed in this study.

An autoregressive moving average mathematical model of ambient data was established, parameters of low-frequency oscillation were designed and parameters of low-frequency oscillation were estimated via DFT curve fitting. The variational modal decomposition method is used to filter direct current components in ambient data signals to improve the accuracy of identification. Simulation phasor measurement unit data and measured data of the power grid proved the correctness of this method.

Compared with the modified extended Yule-Walker method, the proposed approach demonstrates the advantages of fast calculation speed and high accuracy.

Modal identification method of low-frequency oscillation based on ambient data demonstrated high precision and short running time for small interference patterns. This study provides a new research idea for low-frequency oscillation analysis and early warning of power systems.

This paper focuses on assembly line performance of an automotive body shop that builds body‐in‐white (BIW) assembly utilizing about 700+ process robots. These robots perform various operations such as welding, sealing, part handling, stud welding and inspection. There is no accurate tool available for the plant personnel to predict the future throughput based on plant's data. The purpose of this paper is to provide future throughput performance prediction based on plant data using Box‐Jenkins' ARMA model.

The following data were collected for five major assembly lines. First, the assembly machine‐in‐cycle time: the assembly line machines include robots that perform various functions like load, welding or sealing and unloading parts; the manual operators loading cycle time to the production fixtures. The conveyors act as buffers in between stations, and also feed to the production cells, and carry parts from station to station. The conveyors' downtime and uptime were also part of the machine‐in‐cycle time; second, the number of units produced from the beginning to the end of the assembly line; third, the number of fault occurrences in the assembly line due to various machine breakdowns; fourth, the machine availability percentage – i.e. the machine is readily available to perform its functions (the machine blocked upstream (starving) and blocked down (downstream) state is considered here); fifth, the actual efficiency of the machine measured in percentage based on output percentage; sixth, the expected number of units at designed efficiency.

In summary, this research paper provided a systematic development of a forecast model based on Box‐Jenkin's ARMA methodology to analyze the complex assembly line process performance data. The developed ARMA forecast models proved that the future prediction can be accurately predicted based on the past plant performance data. The developed ARMA forecast models predicted the future throughput performance within 99.52 percent accuracy. The research findings were validated by the actual plant performance data.

In this study, the automotive assembly process machines (robots, conveyors and fixtures) production data were collected, statistically analyzed and verified for viable ARMA model verification. The verified ARMA model has been used to predict the plant future months' throughput with 99.52 percent accuracy, based on the plant production data. This research is unique because of its practical usage to improve production.

A novel asynchronous network-based model is proposed in this paper for the sentiment analysis of online public opinions. This new model provides a new approach to analyze the evolution characteristics of online public opinion sentiments in complex environment.

Firstly, a new sentiment analysis model is proposed based on the asynchronous network theory. Then the graphical evaluation and review technique is employed and extended to design the model-based sentiment analysis algorithms. Finally, simulations and real-world case studies are given to show the effectiveness of the proposed model.

The dynamics of online public opinion sentiments are determined by both personal preferences to certain topics and the complex interactive influences of environmental factors. The application of appropriate quantitative models can improve the prediction of public opinion sentiment.

The proposed model-based algorithms provide simple but effective ways to explore the complex dynamics of online public opinions. Case studies highlight the role of government agencies in shaping sentiments of public opinions on social topics.

This paper proposes a new asynchronous network model for the dynamic sentiment analysis of online public opinions. It extends the previous static models and provides a new way to extract opinion evolution patterns in complex environment. Applications of the proposed model provide some new insights into the online public opinion management.