scholarly journals Analysis of Factors Influencing Effective Utilization Coefficient of Irrigation Water in the Manas River Basin

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 189
Author(s):  
Lili Yang ◽  
Tong Heng ◽  
Guang Yang ◽  
Xinchen Gu ◽  
Jiaxin Wang ◽  
...  
Keyword(s):  
Irrigation Water ◽  
Large Scale ◽  
Grey Model ◽  
Analytic Hierarchy ◽  
Effective Utilization ◽  
Factors Affecting ◽  
Factors Influencing ◽  
Utilization Coefficient ◽  
Using Data ◽  
Effective Use

The factors influencing the effective utilization coefficient of irrigation water are not understood well. It is usually considered that this coefficient is lower in areas with large-scale irrigation. With this background, we analyzed the effective utilization coefficient of irrigation water using the analytic hierarchy process using data from 2014 to 2019 in Shihezi City, Xinjiang. The weights of the influencing factors on the effective utilization coefficient of irrigation water in different irrigation areas were analyzed. Predictions of the coefficient’s values for different years were made by understanding the trends based on the grey model. The results show that the scale of the irrigation area is not the only factor determining the effective utilization coefficient of irrigation water. Irrigation technology, organizational integrity, crop types, water price management, local economic level, and channel seepage prevention are the most critical factors affecting the effective use of irrigation water. The grey model prediction results show that the effective utilization coefficient of farmland irrigation water will continuously increase and reach 0.7204 in 2029. This research can serve as a reference for government authorities to make scientific decisions on water-saving projects in irrigation districts in terms of management, operation, and investment.

scholarly journals Calculation and Analysis of Effective Utilization Coefficients of Farmland Irrigation Water in Guizhou Province

2021 ◽  
Vol 233 ◽  
pp. 01060
Author(s):  
Haikuan Wu ◽  
Meiyun An ◽  
Changjun Cai
Keyword(s):  
Irrigation Water ◽  
Water Distribution ◽  
Water Saving ◽  
Guizhou Province ◽  
Irrigation Planning ◽  
Internal Factors ◽  
Effective Utilization ◽  
Factors Influencing ◽  
Important Index ◽  
Utilization Coefficient

Effective utilization coefficient of farmland irrigation water is an important index for water-saving level evaluation. Tracked calculation of effective utilization coefficients of farmland irrigation water as well as quantitative and qualitative analyses in terms of its scientificity and rationality have a guiding role in sustainable development of water-saving irrigation. This paper sets out to calculate the effective utilization coefficients of farmland irrigation water in Guizhou Province in 2019 and evaluate rationality of the results. More than that, some internal factors influencing utilization coefficients are also investigated, and relevant proposals are put forward, in a view to providing technical support for water-saving irrigation planning and scientific water distribution.

scholarly journals Socioeconomic Risks and Their Impacts on Ecological River Health in South Korea: An Application of the Analytic Hierarchy Process

2021 ◽  
Vol 13 (11) ◽  
pp. 6287
Author(s):  
Suyeon Kim ◽  
Sang-Woo Lee ◽  
Se-Rin Park ◽  
Yeeun Shin ◽  
Kyungjin An
Keyword(s):  
South Korea ◽  
Socioeconomic Factors ◽  
Ecosystem Health ◽  
Relative Importance ◽  
River Ecosystem ◽  
River Health ◽  
Analytic Hierarchy ◽  
Factors Affecting ◽  
Factors Influencing ◽  
Hierarchy Process

It is imperative to develop a methodology to identify river impairment sources, particularly the relative impact of socioeconomic sources, to enhance the efficiency of various river restoration schemes and policies and to have an internal diagnosis system in place. This study, therefore, aims to identify and analyze the relative importance of the socioeconomic factors affecting river ecosystem impairment in South Korea. To achieve this goal, we applied the Analytical Hierarchy Process (AHP) to evaluate expert judgement of the relative importance of different socioeconomic factors influencing river ecosystem impairment. Based on a list of socioeconomic factors influencing stream health, an AHP questionnaire was prepared and administered to experts in aquatic ecology. Our analysis reveals that secondary industries form the most significant source of stream ecosystem impairment. Moreover, the most critical socioeconomic factors affecting stream impairment are direct inflow pollution, policy implementation, and industrial wastewater. The results also suggest that the AHP is a rapid and robust approach to assessing the relative importance of different socioeconomic factors that affect river ecosystem health. The results can be used to assist decision makers in focusing on actions to improve river ecosystem health.

scholarly journals Factors influencing the performance of community health workers: A qualitative study of Anganwadi Workers from Bihar, India

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242460
Author(s):  
Aparna John ◽  
Nicholas Nisbett ◽  
Inka Barnett ◽  
Rasmi Avula ◽  
Purnima Menon
Keyword(s):  
Qualitative Study ◽  
Community Health ◽  
Large Scale ◽  
Health Workers ◽  
Seasonal Migration ◽  
High Coverage ◽  
Factors Affecting ◽  
Community Factors ◽  
Factors Influencing ◽  
The Individual

Globally, there remain significant knowledge and evidence gaps around how to support Community Health Worker (CHW) programmes to achieve high coverage and quality of interventions. India’s Integrated Child Development Services scheme employs the largest CHW cadre in the world—Anganwadi Workers (AWWs). However, factors influencing the performance of these workers remain under researched. Lessons from it have potential to impact on other large scale global CHW programmes. A qualitative study of AWWs in the Indian state of Bihar was conducted to identify key drivers of performance in 2015. In-depth interviews were conducted with 30 AWWs; data was analysed using both inductive and deductive thematic analysis. The study adapted and contextualised existing frameworks on CHW performance, finding that factors affecting performance occur at the individual, community, programme and organisational levels, including factors not previously identified in the literature. Individual factors include initial financial motives and family support; programme factors include beneficiaries’ and AWWs’ service preferences and work environment; community factors include caste dynamics and community and seasonal migration; and organisational factors include corruption. The initial motives of the worker (the need to retain a job for family financial needs) and community expectations (for product-oriented services) ensure continued efforts even when her motivation is low. The main constraints to performance remain factors outside of her control, including limited availability of programme resources and challenging relationships shaped by caste dynamics, seasonal migration, and corruption. Programme efforts to improve performance (such as incentives, working conditions and supportive management) need to consider these complex, inter-related multiple determinants of performance. Our findings, including new factors, contribute to the global literature on factors affecting the performance of CHWs and have wide application.

Identifying and ranking the effective factors on customer values

2014 ◽  
Vol 7 (1) ◽  
pp. 57-74 ◽  
Author(s):  
Mahsa Ghandehary ◽  
Hojjat Harati ◽  
Javad Khazaei Pool
Keyword(s):  
Customer Value ◽  
Banking Industry ◽  
Time Factors ◽  
Analytic Hierarchy ◽  
Content Type ◽  
Factors Affecting ◽  
Effective Factors ◽  
Factors Influencing ◽  
Relational Benefits ◽  
Customer Values

Purpose – The purpose of this study is to identify and rank the effective factors on customer values from the perspective of banking customers. Design/methodology/approach – This study is a practical and descriptive survey. In order to rank the factors affecting customer values, a fuzzy analytical hierarchical process has been used. The data were gathered through Delphi method and questionnaires. Findings – The results of this study indicate that the factors affecting customer values include costs, relational benefits, brand perceptions, and services quality. These factors influence customer values, respectively. The results also indicate that financial costs among costs, operational benefit among relational benefits, trust in employee behavior in customer brand perceptions, and finally confidence in service quality dimensions have the highest priority. Research limitations/implications – The proposed model and research findings will greatly help researchers and practitioners understand the factors influencing customer value using a fuzzy analytic hierarchy process (FAHP) in the banking industry. Originality/value – In this study, for the first time, factors influencing customer value are studied by using a FAHP in the banking industry. The use of this method in this study has a certain authenticity.

Analysis on the influencing factors of effective utilization coefficient of irrigation water in irrigation districts based on Horton fractal theory

2019 ◽  
Vol 19 (6) ◽  
pp. 1695-1703
Author(s):  
Dong Liu ◽  
Dandan Guo ◽  
Qiang Fu ◽  
Mo Li ◽  
Chunlei Liu ◽  
...  
Keyword(s):  
Irrigation Water ◽  
Fractal Theory ◽  
Fractal Dimensions ◽  
System Structure ◽  
Irrigation District ◽  
Gray Relational Analysis ◽  
Effective Utilization ◽  
Irrigation Districts ◽  
Utilization Coefficient ◽  
The Relationship

Abstract To study the relationship between the effective utilization coefficient of irrigation water and canal system structure including the influences of various factors in irrigation districts, the fractal dimensions of each irrigation district by Horton's law were calculated by using canal data from 20 typical irrigation districts in Heilongjiang Province. The results showed that the fractal dimensions of the three-level irrigation districts were within the general range of the Horton water law. Of the four-level irrigation districts, only the fractal dimension of the Wutong River irrigation district was 3.037, which was beyond the upper limit of approximately 1.23%. Using gray relational analysis, the correlation degrees of four factors such as the effective irrigated area, the water-saving area ratio, the complexity of the canal structure, the channel density with the effective utilization coefficient of irrigation water and the fractal dimensions were all above 0.5 in the three- and four-level irrigation districts.

scholarly journals Research on Irrigation Water Efficiency of Guizhou Province Based on SFA

2021 ◽  
Author(s):  
Kang Zhang ◽  
Yunduo Zhang ◽  
Jiangli Zheng ◽  
Zhipeng Ma
Keyword(s):  
Technical Efficiency ◽  
Irrigation Water ◽  
Water Saving ◽  
Guizhou Province ◽  
Water Efficiency ◽  
Effective Utilization ◽  
Utilization Coefficient ◽  
The Difference ◽  
Large Water ◽  
New Perspective

The measurement of farmland irrigation water efficiency is an important part of the evaluation of agricultural water saving. Since the method of econometrics research on the relationship between input and output was introduced to the evaluation of irrigation water efficiency, it has provided a new perspective for evaluating irrigation water efficiency. This study takes Guizhou in southwest China as an example, using the SFA method to calculate the technical efficiency of food production and irrigation water from 2011 to 2018, and compares the technical efficiency of irrigation water with the measured irrigation water effective utilization coefficient analysis. The analysis shows that: (1) Guizhou’s multi-year average irrigation water technical efficiency value is 0.730, and the difference between cities is large. 20.8% of the urban irrigation water technical efficiency is lower than the average level, and there is a large water saving potential; (2) The correlation coefficient between the irrigation water technical efficiency calculated by the SFA method and the measured irrigation water effective utilization coefficient is 0.804, which is highly positively correlated, and the simulation calculation validity is 70.6%; (3) The SFA method can provide an effective reference for the study of the change trend of the effective utilization coefficient of farmland irrigation water in the absence of measured data.

scholarly journals Computational and experimental performance of CRISPR homing gene drive strategies with multiplexed gRNAs

2019 ◽  
Author(s):  
Samuel E. Champer ◽  
Suh Yeon Oh ◽  
Chen Liu ◽  
Zhaoxin Wen ◽  
Andrew G. Clark ◽  
...  
Keyword(s):  
Large Scale ◽  
Optimal Number ◽  
Activity Level ◽  
Gene Drive ◽  
Factors Affecting ◽  
Homology Directed Repair ◽  
Scale Population ◽  
And Performance ◽  
Using Data ◽  
Gene Drives

ABSTRACTCRISPR homing gene drives potentially have the capacity for large-scale population modification or suppression. However, resistance alleles formed by the drives can prevent them from successfully spreading. Such alleles have been found to form at high rates in most studies, including those in both insects and mammals. One possible solution to this issue is the use of multiple guide RNAs (gRNAs), thus allowing cleavage by the drive even if resistance sequences are present at some of the gRNA target sequences. Here, we develop a high-fidelity model incorporating several factors affecting the performance of drives with multiple gRNAs, including timing of cleavage, reduction in homology-directed repair efficiency due to imperfect homology around the cleavage site, Cas9 activity saturation, variance in the activity level of individual gRNAs, and formation of resistance alleles due to incomplete homology-directed repair. We parameterize the model using data from homing drive experiments designed to investigate these factors and then use it to analyze several types of homing gene drives. We find that each type of drive has an optimal number of gRNAs, usually between two and eight, dependent on drive type and performance parameters. Our model indicates that utilization of multiple gRNAs is insufficient for construction of successful gene drives, but that it provides a critical boost to drive efficiency when combined with other strategies for population modification or suppression.

scholarly journals Factors influencing Vietnam’s handicraft export with the gravity model

2019 ◽  
Vol 21 (2) ◽  
pp. 156-171
Author(s):  
Luong Anh Thu ◽  
Sun Fang ◽  
Sham Sunder Kessani
Keyword(s):  
Gravity Model ◽  
Large Scale ◽  
Past Research ◽  
International Markets ◽  
Content Type ◽  
Factors Affecting ◽  
Factors Influencing ◽  
The World ◽  
Trading Partners ◽  
The Government

Purpose The purpose of this paper is to investigate the factors that affect handicraft export from Vietnam to trading partners in the period 2007–2017, and how those factors influence the export of handicraft products of Vietnam. Design/methodology/approach The research uses the approach of gravity model based on panel data to evaluate the export of Vietnamese handicraft to 50 main trading partners, covering the period from 2007 to 2017. Findings The estimated results reveal that Vietnam’s GDP, importer’s GDP, trading partner’s population, Vietnam’s inflation, the economic distance between Vietnam and importer, the openness of Vietnam, importing country’s common language and the issue that both Vietnam and importer are member of APEC are the main factors affecting Vietnamese handicraft export. Research limitations/implications This study also has some limitations. It is limited in the data, as some other areas in the world have not been observed and included in the research. In the future, a study with large-scale data of space and time should be conducted, which will certainly give a universal result and fewer errors. However, this paper, in our opinion, provides a significant result and may help the government and policy makers to undertake appropriate measures to improve and promote the export of Vietnamese handicrafts to the world markets. Practical implications The research describes the current situation, and it studies factors influencing Vietnam’s handicraft export using the qualitative analysis. The result should be useful for the policy maker and enterprises to promote export activities of Vietnamese handicrafts to international markets. Social implications Handicraft export of Vietnam plays an important part in retaining the culture value and social development as well as encouraging sustainable development for the rural poor within the country. Originality/value The past research related to Vietnamese handicraft export almost analyzed the situation to promote export handicrafts. This research is based on the study of factors affecting trade and the gravity model to elaborate and supplement the factors that affect the export of handicraft in accordance with the actual conditions of Vietnam.

scholarly journals A Hybrid GA–PSO–CNN Model for Ultra-Short-Term Wind Power Forecasting

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6500
Author(s):  
Jie Liu ◽  
Quan Shi ◽  
Ruilian Han ◽  
Juan Yang
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Pso Algorithm ◽  
Stable Operation ◽  
Feature Maps ◽  
Wind Power Forecasting ◽  
Factors Affecting ◽  
Using Data ◽  
Power Forecasting

Accurate and timely wind power forecasting is essential for achieving large-scale wind power grid integration and ensuring the safe and stable operation of the power system. For overcoming the inaccuracy of wind power forecasting caused by randomness and volatility, this study proposes a hybrid convolutional neural network (CNN) model (GA–PSO–CNN) integrating genetic algorithm (GA) and a particle swarm optimization (PSO). The model can establish feature maps between factors affecting wind power such as wind speed, wind direction, and temperature. Moreover, a mix-encoding GA–PSO algorithm is introduced to optimize the network hyperparameters and weights collaboratively, which solves the problem of subjective determination of the optimal network in the CNN and effectively prevents local optimization in the training process. The prediction effectiveness of the proposed model is verified using data from a wind farm in Ningxia, China. The results show that the MAE, MSE, and MAPE of the proposed GA–PSO–CNN model decreased by 1.13–9.55%, 0.46–7.98%, and 3.28–19.29%, respectively, in different seasons, compared with Single–CNN, PSO–CNN, ISSO–CNN, and CHACNN models. The convolution kernel size and number in each convolution layer were reduced by 5–18.4% in the GA–PSO–CNN model.

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