Research on the E-commerce Model in Textile Industry

E-commerce will play an important role in textile industry. Yet the proper e-commerce model in textile industry has not been solved up till now. It is necessary to study the model as soon as possible, so that we may get together with the advanced countries

Online Customer Service System Using Hybrid Model

In a traditional customer service support environment, service engineers typically provide a worldwide customer base support through the use of telephone calls. Such a mode of support is inefficient, ineffective and generally results in high costs, long service cycles, and poor quality of service. The rapid growth of the World Wide Web and Intelligent Agent technology, with its widespread acceptance and accessibility, have resulted in the emergence of Web-based and AI Agent-based systems. Depending on the functionality provided by such systems, most of the associated disadvantages of the traditional customer service support environment can be eliminated. This paper describes a framework for Web-based and AI Agent-based online customer service support system, and discusses the method to use Rough Set Theory and Neural Network Theory to support intelligent fault diagnosis by customers or service engineers

Multi-Objective Optimization for Reservoir Operation Considering Water Diversion and Power Generation Objectives

Due to the uneven distribution of water resources in time and space, the problem of water shortage has become increasingly serious in some areas. To optimize use of water resources, it is urgent to establish multi-objective models and apply effective optimization algorithms to guide reservoir management. This study proposed a model of multi-objective optimization for reservoir operation (MORO) with the objectives of maximizing water diversion and power generation. The multi-objective evolutionary algorithm based on decomposition with adaptive weight vector adjustment (MOEA/D-AWA) was applied to solve the MORO problem. In addition, the performance of the MOEA/D-AWA was compared with two other algorithms based on the hyper-volume index. Huangjinxia reservoir, which is located in Shaanxi, China, was selected as the case study. The results show that: (1) the proposed model is effective and reasonable in theory; (2) the optimization results obtained by MOEA/D-AWA demonstrate this algorithm can be applied to the MORO problem, providing a set of evenly distributed non-dominated solutions; and (3) water diversion and power generation are indeed contradictory objectives. The MORO strategy can be used to efficiently utilize water resources, improve the comprehensive benefits of reservoirs, and provide decision support for actual reservoir operation

Comparison of hybrid machine learning models to predict short-term meteorological drought in Guanzhong region, China

Reliable drought prediction plays a significant role in drought management. Applying machine learning models in drought prediction is getting popular in recent years, but applying the stand-alone models to capture the feature information is not sufficient enough, even though the general performance is acceptable. Therefore, the scholars tried the signal decomposition algorithm as a data pre-processing tool, and coupled it with the stand-alone model to build ‘decomposition-prediction’ model to improve the performance. Considering the limitations of using the single decomposition algorithm, an ‘integration-prediction’ model construction method is proposed in this study, which deeply combines the results of multiple decomposition algorithms. The model tested three meteorological stations in Guanzhong, Shaanxi Province, China, where the short-term meteorological drought is predicted from 1960 to 2019. The meteorological drought index selects the Standardized Precipitation Index on a 12-month time scale (SPI-12). Compared with stand-alone models and ‘decomposition-prediction’ models, the ‘integration-prediction’ models present higher prediction accuracy, smaller prediction error and better stability in the results. This new ‘integration-prediction’ model provides attractive value for drought risk management in arid regions. HIGHLIGHTS Machine learning model has great value in short-term meteorological drought prediction.; Signal decomposition algorithm as a data pre-processing tool can significantly improve the prediction performance of machine learning model.; Deeply combining the results of multiple decomposition algorithms could achieve higher prediction accuracy.; The ‘integration-prediction’ model provides a new way for drought prediction in arid regions.

Soil Moisture Characteristics of a Typical Slope in the Watershed of the Loess Plateau for Gully Land Consolidation Project

To clarify the characteristics of soil moisture in the slope of watershed of the gully land consolidation watershed, and to further guide the implementation of the gully land consolidation project and vegetation restoration in this area, this study selected a typical slope of gully land consolidation watershed as the research object. The soil moisture of different slope positions was monitored and analyzed, and its temporal stability was analyzed. The results showed that: 1) The average soil moisture of different slope positions increased with the increase of soil depth, and the variability showed an increasing-decreasing-increasing trend, and the variability was the smallest at about 70 cm from the surface with weak variability, and the soil moisture variability in other layers is moderate. 2) On the slope, the distribution characteristics of soil moisture content were as follows: upslope <middleslope <downslope position. The differences of soil moisture between the upslope and downslope, midslope and downslope were significant. 3) The temporal stability analysis of the soil moisture showed that there is high stability between August and September of the soil moisture of the 0-50 cm and the correlation is extremely significant, while the soil moisture content of 50-100 cm range has a significant correlation between May and June. 4) The time stability of soil moisture in the middle slope position is the highest, followed by the upslope position, and the time stability in the downslope position is the lowest. 5) The best time stability point in the study area is the M3 point of the middle slope

Monthly Runoff Interval Prediction Based on Fuzzy Information Granulation and Improved Neural Network

High-precision monthly runoff prediction results are of great significance to regional water resource management. However, with the changes in human activity, climate, and underlying surface conditions, the runoff sequence presents highly nonlinear and random characteristics. In order to improve the accuracy of runoff prediction, this study proposed a runoff prediction model based on fuzzy information granulation (FIG) and back propagation neural network (BPNN) improved with genetic algorithm (FIG-GA-BP). First, FIG was used to process the original runoff data to generate three sequences of minimum, average, and maximum that can reflect the rule of runoff changes. Then, genetic algorithms (GA) were used to obtain the optimal initial weights and thresholds of the BPNN through selection, crossover, and mutation. Finally, BPNN was used to predict the generated three sequences separately to obtain the prediction interval. The proposed model was applied to the monthly runoff interval prediction of Linjiacun and Weijiabu hydrological stations in the main stream of the Wei River and Zhangjiashan hydrological station on Jing River, a tributary of the Wei River. Compared with the interval prediction model FIG-BP, FIG-WNN, and traditional BP model. The results show that the FIG-GA-BP interval prediction model had a good prediction effect, with higher prediction accuracy and a narrower range of prediction intervals. Therefore, this model has superiority and practicability in monthly runoff interval prediction

Monthly Runoff Interval Prediction Based on Fuzzy Information Granulation and Improved Neural Network

High-precision monthly runoff prediction results are of great significance to regional water resource management. However, with the changes in human activity, climate, and underlying surface conditions, the runoff sequence presents highly nonlinear and random characteristics. In order to improve the accuracy of runoff prediction, this study proposed a runoff prediction model based on fuzzy information granulation (FIG) and back propagation neural network (BPNN) improved with genetic algorithm (FIG-GA-BP). First, FIG was used to process the original runoff data to generate three sequences of minimum, average, and maximum that can reflect the rule of runoff changes. Then, genetic algorithms (GA) were used to obtain the optimal initial weights and thresholds of the BPNN through selection, crossover, and mutation. Finally, BPNN was used to predict the generated three sequences separately to obtain the prediction interval. The proposed model was applied to the monthly runoff interval prediction of Linjiacun and Weijiabu hydrological stations in the main stream of the Wei River and Zhangjiashan hydrological station on Jing River, a tributary of the Wei River. Compared with the interval prediction model FIG-BP, FIG-WNN, and traditional BP model. The results show that the FIG-GA-BP interval prediction model had a good prediction effect, with higher prediction accuracy and a narrower range of prediction intervals. Therefore, this model has superiority and practicability in monthly runoff interval prediction

The application of electronic commerce on water resource management

Although total water Resources in China are not deficient compared with those in other countries over the world, the effective use efficiency is not high. There is a big wastage either in agricultural water use, in industrial water use or in daily living water use. whereby indicating that water resources management in china is till in backward level or state. E-commerce as a major means of economic trade in the 2lst century has brought about new opportunity for economic growth in the countries over the world. On the one hand, E-commerce uses the internet technology to retransform the enterprise production flow process, marketing flow process and resources management, through which the all social resources can be disposed and used in a more rational way; the production cost of the enterprises can be reduced most greatly; the international competitive power of the enterprises can also be raised; and the new economic growing point of the enterprises can be promoted. On the other hand, water is a kind of natural resources with use value as well as of the commercial product through being processed and transported. For this reason, water should be included in commercial economy, which should be operated or disposed rationally via marketing regulation, thus, providing the possibility for E-commerce to enter water resources management system. This paper deals with the establishment of E-commerce patterns or models for water resources management system by means of combining E-commerce with water resources management system and using water price as an economic means and discusses the possibility, performances and procedures of establishing the E-commerce patterns. Therefore, the E-commerce will provide the applicable operation platform for the E-commerce for water resources management and operation on which water resources development and operation, advocacy and extension as well as information services can be completed, and also on which the inner professional exchanges and cooperations can be carried out so as to satisfy the demands by the decision makers at the different levels. At the same time, facing the international competition, carrying out water resources trade, establishing scientific water price system and realizing the sustainable development strategy of water resources can also be achieved via the E-commerce

Visualization of Multi Scenario Water Resources Regulation Based on a Dualistic Water Cycle Framework

Under the influences of global environmental change, the water cycle exhibits a characteristic &ldquo;natural-social&rdquo; duality. The theoretical framework of this dualistic water cycle model has become relatively mature and the frameworks for the natural and social water cycles of the process description are now relatively clear. Although many studies in this field focus on further improvement of the model, it is difficult to apply it to the multi-scenario regulation of water resources. To address this gap, based on the comprehensive integrated platform, this paper uses visual knowledge map technology and component technology to visualize the theoretical framework of the dualistic water cycle, and establishes a framework system for the visualization of the dualistic water cycle process. Three different water resource regulation scenarios were established in the system and example applications of water resources regulation using the system were realized. The simulation results of the system show that the system intends to assist the business function of water resources regulation, and it is able to set up a number of dynamic scenarios that can be controlled by users and assist the application of regional water resources regulation. The system&rsquo;s regulatory process is visual, trustworthy, and operational, and it realizes the simulation application of water resources regulation under the framework of the dualistic water cycle

Response of Fractal Analysis to Soil Quality Succession in Long-Term Compound Soil Improvement of Mu Us Sandy Land, China

The degraded aeolian sandy soil in China’s Mu Us Sandy Land requires amendment before it can be suitable for maize or other agricultural production. The addition of material from the local “soft” bedrock can create a new compound soil whose particle composition and structural stability are key issues for sustainable soil development in the region. We used field data from 2010 to 2018 to study the variations in fractal characteristics of compound soil particles at soft rock to sand volume ratios of 1 : 1, 1 : 2, and 1 : 5, along with changes in soil organic matter. Over the study period, all three compound soils showed gradual increases in clay and silt content with corresponding decreasing sand content. The fractal dimension (FD) of particles at ratio 1 : 2 increased by 8.8%, higher than those at 1 : 1 (8.6%) and 1 : 5 (7.7%). The organic matter content (OMC) of particles at ratio 1 : 2 reached a maximum (6.24 ± 0.30 g/kg), an increase of 12 times over the original value. The FD and OMC of particles at ratios 1 : 1 and 1 : 5 were less stable but showed overall increase. The 1 : 2 ratio compound soil was most suitable for maize growth as its clear increase in silt and clay content most improved the texture and OMC of the original sandy soil. Such research has important theoretical and practical significance for understanding the evolutionary mechanism and sustainable use of the compound soil in agriculture within the Mu Us Sandy Land