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In order to scientifically allocate rural landscape resources, reasonably plan rural tourism space, and ensure that the local characteristics of the countryside are not homogenized when carrying out rural landscape design, this paper studies rural landscape design strategies based on deep learning models. The extreme learning machine algorithm, DBN-RBM algorithm model and the improved DBN-DELM algorithm are the main technical means to obtain research data and parameter calibration results for tourism planning and development work, and the rural planning direction and planning theme is determined through the rural landscape design pre-analysis work. The data show that the main motives of tourists’ rural experience tourism are close to nature 85.90% and leisure vacation 75%, followed by understanding culture 45.30%, novelty 30.70%, parent-child education 29.20%, health retreat 30.40%, and business meeting 5.90%. In this paper, the study of rural landscape planning and design can effectively alleviate the contradiction between people’s production and living and ecological environment and coordinate the benign development of rural and tourism elements in their respective spaces.

Background: Early dislocation following primary total hip arthroplasty (THA) is a rare but devastating complication and represents a source of patient morbidity and financial burden to the healthcare system. The objective of this study was to identify patient characteristics and comorbidities that are associated with increased early in-hospital dislocation rates following primary THA. Methods: A retrospective cohort study was conducted using patient data from the Nationwide Inpatient Sample (NIS) database; we identified patients who had undergone THA from 2016 to 2019 and compared those with an early periprosthetic dislocation prior to discharge to those without. The patient characteristics and comorbidities were compared using univariate analysis with a subsequent investigation of statistically significant variables using multivariate analysis. The variables were compared using chi square, Fisher's exact test, and independent sample t-tests with data assessed using odds ratio with 95% confidence intervals. Results: A total of 5151 patients sustained an early dislocation compared to 362,743 who did not. Those who sustained an in-hospital dislocation were more likely to share the following characteristics: female sex (OR 1.21, p < 0.01), age > 70 (OR 1.45, p < 0.01), Caucasian ethnicity (OR 1.22, p < 0.01), SLE (OR 1.87, p < 0.01), and Parkinson's disease (OR 1.93, p < 0.01). Certain characteristics were also associated with decreased odds of having an in-hospital dislocation including elective surgery (OR 0.14, p < 0.01), tobacco use (OR 0.8, p < 0.01), diabetes without complications (OR 0.87, p < 0.01), and a history of heart valve replacement (OR 0.81, p < 0.01). The length of stay was significantly longer (4.7 days vs. 2.3 days) as was the total hospital charges (USD $101,517 vs. USD $66,388) for the early in-hospital dislocation group. Conclusions: Several patient characteristics and comorbidities are associated with early in-hospital dislocation episodes following total hip arthroplasty including female sex, age > 70, non-elective surgery, SLE, and Parkinson's. This information may be useful to help guide intraoperative implant selection and/or postoperative protocol in select patient populations to limit early instability as well as decrease the financial burden associated with this postoperative complication. [ABSTRACT FROM AUTHOR]