scholarly journals Dynamic Forecasting Algorithm of Inbound Ice and Snow Tourism in China Based on Improved Deep Confidence Network

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yuguang Zhao ◽  
Chuanming Jiao ◽  
Jinhui Li ◽  
Zhigang Yuan ◽  
Xin Li ◽  
...  
Keyword(s):  
Prediction Model ◽  
Tourism Demand ◽  
Dynamic Prediction ◽  
National Economic ◽  
Sample Data ◽  
Inbound Tourism ◽  
Dynamic Forecasting ◽  
Supply Capacity ◽  
Improved Accuracy ◽  
The Relationship

Ice and snow-based tourism is getting popular around the world and it is one of the major sources of revenue for a region with required facilities. According to a report by China Daily, China was expected to witness 230 million tourist visits in 2020-2021 with a total revenue generation surpassing 390 billion yuan. In order to promote the ice and snow tourism, proper arrangements such as accommodation, transport facility, and energy provision for heating and food need to be arranged as per the demand of the visitors at a certain period of time. A tourist prediction system can help in this regard for good estimation but considering the problems of traditional ice and snow tourism systems, specifically the prediction accuracy and long forecasting time, a dynamic forecasting algorithm for ice and snow inbound tourism based on an improved deep confidence network is proposed. The system analyzes the relationship between the demand for ice and snow inbound tourism and the level of national economic development, people’s living standards, demographic characteristics, traffic conditions, and tourism supply capacity. It then takes the influencing factors of ice and snow inbound tourism demand as sample data and arranges the sample data sequence. The inbound tourism demand dynamic prediction model uses an improved deep confidence network to learn and train the prediction model, input test data into the trained model, and output the dynamic prediction value of ice and snow inbound tourism demand in the output layer to obtain the prediction result. The simulation results show that the proposed algorithm has improved accuracy in predicting the demand of inbound tourism for ice and snow, whereas the forecasting time is reduced.

Inbound tourism and the marketization of China’s institutions

2016 ◽  
Vol 7 (4) ◽  
pp. 542-554
Author(s):  
Jing Ma ◽  
Shuo Liu
Keyword(s):  
Panel Data ◽  
Dynamic Panel Data ◽  
Economic Resources ◽  
Tourism Demand ◽  
Demand Model ◽  
Content Type ◽  
International Recognition ◽  
China Market ◽  
Inbound Tourism ◽  
The Relationship

Purpose The purpose of this paper is to examine whether the institutions play a role in tourism development and international recognition, specifically the influence of marketization on the international tourists’ inbound arrivals in different Chinese provinces. Design/methodology/approach This paper constructs a demand model of tourism and empirically analyzes the relationship between marketization and inbound tourism demand with the panel data of the provinces of China and NERI Index of Marketization. Findings Marketization does have an influence on inbound tourism demand of China. Specially, the relationship between government and market, the development of product market, the market intermediary organizations and the legal system environment can increase the demand of the foreign tourists to visit China, although the magnitudes are different. Practical implications This paper argues that the qualities of marketization intuitions are important in increasing inbound tourism, given that it can bring better tourism experience and improve the international recognition. Strengthening the legislation and protecting the legitimate rights and interests of consumers can attract more international travelers to China. Market distribution of competitive economic resources, reducing political intervention into corporate activities and relieving tax burdens of enterprises can improve the competitiveness and the service qualities of Chinese domestic tourism firms. Originality/value This paper leads the discussions of institutions and tourism. It combines the consumer theory and uses static and dynamic panel data models to analyze the influencing factors of Chinese tourism. It argues that Chinese inbound tourism shall develop with the systemic marketization progress in China.

scholarly journals Modeling and Analysis on Teacher-Student Relationship

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Li Xu ◽  
Qi Yang
Keyword(s):  
Discrete Model ◽  
Equation Model ◽  
Reciprocal Relations ◽  
Modeling And Analysis ◽  
Teacher Student ◽  
Teacher Student Relationship ◽  
Sample Data ◽  
Teachers And Students ◽  
Student Relationship ◽  
The Relationship

Although the teacher-student relationship has been addressed in some studies, the cooperation or reciprocal relations between teachers and students have not been explored sufficiently. In this paper, a difference equation model is applied to express the relationship, stability analysis at the positive steady state of the discrete model is done to verify that the performance output is not empty, and hypothesis testing is conducted to show the validity of the model by means of sample data from a college. Then some reasonable suggestions are proposed to improve the performance output of teachers and students.

scholarly journals Dynamic prediction for clinically relevant pancreatic fistula: a novel prediction model for laparoscopic pancreaticoduodenectomy

BMC Surgery ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Runwen Liu ◽  
Yunqiang Cai ◽  
He Cai ◽  
Yajia Lan ◽  
Lingwei Meng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Pancreatic Fistula ◽  
Postoperative Pancreatic Fistula ◽  
Laparoscopic Pancreaticoduodenectomy ◽  
C Reactive Protein ◽  
Dynamic Prediction ◽  
Albumin Ratio ◽  
Drainage Fluid ◽  
Reactive Protein ◽  
Baseline Factors

Abstract Background With the recent emerge of dynamic prediction model on the use of diabetes, cardiovascular diseases and renal failure, and its advantage of providing timely predicted results according to the fluctuation of the condition of the patients, we aim to develop a dynamic prediction model with its corresponding risk assessment chart for clinically relevant postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy by combining baseline factors and postoperative time-relevant drainage fluid amylase level and C-reactive protein-to-albumin ratio. Methods We collected data of 251 patients undergoing LPD at West China Hospital of Sichuan University from January 2016 to April 2019. We extracted preoperative and intraoperative baseline factors and time-window of postoperative drainage fluid amylase and C-reactive protein-to-albumin ratio relevant to clinically relevant pancreatic fistula by performing univariate and multivariate analyses, developing a time-relevant logistic model with the evaluation of its discrimination ability. We also established a risk assessment chart in each time-point. Results The proportion of the patients who developed clinically relevant postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy was 7.6% (19/251); preoperative albumin and creatine levels, as well as drainage fluid amylase and C-reactive protein-to-albumin ratio on postoperative days 2, 3, and 5, were the independent risk factors for clinically relevant postoperative pancreatic fistula. The cut-off points of the prediction value of each time-relevant logistic model were 14.0% (sensitivity: 81.9%, specificity: 86.5%), 8.3% (sensitivity: 85.7%, specificity: 79.1%), and 7.4% (sensitivity: 76.9%, specificity: 85.9%) on postoperative days 2, 3, and 5, respectively, the area under the receiver operating characteristic curve was 0.866 (95% CI 0.737–0.996), 0.896 (95% CI 0.814–0.978), and 0.888 (95% CI 0.806–0.971), respectively. Conclusions The dynamic prediction model for clinically relevant postoperative pancreatic fistula has a good to very good discriminative ability and predictive accuracy. Patients whose predictive values were above 14.0%, 8.3%, and 7.5% on postoperative days 2, 3, and 5 would be very likely to develop clinically relevant postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy.

A dynamic prediction model for time‐to‐peak

2021 ◽  
Author(s):  
Mistaya Langridge ◽  
Ed McBean ◽  
Hossein Bonakdari ◽  
Bahram Gharabaghi
Keyword(s):  
Prediction Model ◽  
Dynamic Prediction ◽  
Time To Peak

The Significance of Regional Studies for the Elaboration of National Economic History

1960 ◽  
Vol 20 (4) ◽  
pp. 588-596 ◽  
Author(s):  
Henry W. Broude
Keyword(s):  
Economic Development ◽  
Economic History ◽  
Regional Differentiation ◽  
Regional Studies ◽  
National Economic ◽  
Methodological Problems ◽  
National Economic Development ◽  
Relationship Of ◽  
The Relationship ◽  
Point Of Departure

The purpose of this paper is to serve as a point of departure for discussion of the relationship of regional differentiation and growth to general economic development. In addition to touching on methodological problems, I hope to establish two specific points: (a) that the needs of economic history call for particular perspectives in delimiting regions, and (b) that study of regional interaction can provide insights in an understanding of national economic development.

The Relationship Between Domestic Partner Violence and Suicidal Behaviors in an Adult Community Sample: Examining Hope Agency and Pathways as Protective Factors

2017 ◽  
Vol 24 (12) ◽  
pp. 1399-1412
Author(s):  
Edward C. Chang ◽  
Elizabeth A. Yu ◽  
Emma R. Kahle ◽  
Yifeng Du ◽  
Olivia D. Chang ◽  
...  
Keyword(s):  
Protective Factors ◽  
Prediction Model ◽  
Partner Violence ◽  
Community Sample ◽  
Suicidal Behaviors ◽  
Interactive Model ◽  
Independent Effects ◽  
The Relationship ◽  
Additional Variance

We examined an additive and interactive model involving domestic partner violence (DPV) and hope in accounting for suicidal behaviors in a sample of 98 community adults. Results showed that DPV accounted for a significant amount of variance in suicidal behaviors. Hope further augmented the prediction model and accounted for suicidal behaviors beyond DPV. Finally, we found that DPV significantly interacted with both dimensions of hope to further account for additional variance in suicidal behaviors above and beyond the independent effects of DPV and hope. Implications for the role of hope in the relationship between DPV and suicidal behaviors are discussed.

scholarly journals Predicting Swiss Needle Cast Disease Distribution and Severity in Young Douglas-Fir Plantations in Coastal Oregon

2003 ◽  
Vol 93 (7) ◽  
pp. 790-798 ◽  
Author(s):  
Pablo H. Rosso ◽  
Everett M. Hansen
Keyword(s):  
Prediction Model ◽  
Disease Severity ◽  
Pacific Northwest ◽  
Regression Tree ◽  
Douglas Fir ◽  
Slope Aspect ◽  
Disease Prediction ◽  
Needle Cast ◽  
Swiss Needle Cast ◽  
The Relationship

Swiss needle cast (SNC), caused by the fungus Phaeocryptopus gaeumannii, is producing extensive defoliation and growth reduction in Douglas-fir forest plantations along the Pacific Northwest coast. An SNC disease prediction model for the coastal area of Oregon was built by establishing the relationship between the distribution of disease and the environment. A ground-based disease survey (220 plots) was used to study this relationship. Two types of regression approaches, multiple linear regression and regression tree, were used to study the relationship between disease severity and climate, topography, soil, and forest stand characteristics. Fog occurrence, precipitation, temperature, elevation, and slope aspect were the variables that contributed to explain most of the variability in disease severity, as indicated by both the multiple regression (r 2 = 0.57) and regression tree (RMD = 0.27) analyses. The resulting regression model was used to construct a disease prediction map. Findings agree with and formalize our previous understanding of the ecology of SNC: warmer and wetter conditions, provided that summer temperatures are relatively low, appear to increase disease severity. Both regression approaches have characteristics that can be useful in helping to improve our understanding of the ecology of SNC. The prediction model is able to produce a continuous prediction surface, suitable for hypothesis testing and assisting in disease management and research.

scholarly journals Analyzing the Influence of Transportations on Chinese Inbound Tourism: Markov Switching Penalized Regression Approaches

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 515
Author(s):  
Woraphon Yamaka ◽  
Xuefeng Zhang ◽  
Paravee Maneejuk
Keyword(s):  
Structural Changes ◽  
Markov Switching ◽  
Penalized Regression ◽  
Tourism Demand ◽  
Switching Regression ◽  
Inbound Tourism ◽  
Dimensional Parameters ◽  
Demand Equations ◽  
Transportation Development ◽  
Positive Effect

This study investigates the nonlinear impact of various modes of transportation (air, road, railway, and maritime) on the number of foreign visitors to China originating from major source countries. Our nonlinear tourism demand equations are determined through the Markov-switching regression (MSR) model, thereby, capturing the possible structural changes in Chinese tourism demand. Due to many variables and the limitations from the small number of observations confronted in this empirical study, we may face multicollinearity and endogeneity bias. Therefore, we introduce the two penalized maximum likelihoods, namely Ridge and Lasso, to estimate the high dimensional parameters in the MSR model. This investigation found the structural changes in all tourist arrival series with significant coefficient shifts in transportation variables. We observe that the coefficients are relatively more significant in regime 1 (low tourist arrival regime). The coefficients in regime 1 are all positive (except railway length in operation), while the estimated coefficients in regime 2 are positive in fewer numbers and weak. This study shows that, in the process of transportation, development and changing inbound tourism demand from ten countries, some variables with the originally strong positive effect will have a weak positive effect when tourist arrivals are classified in the high tourist arrival regime.

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