A stage-structured hierarchical Bayes model for the babassu palm tree population dynamics – Estimated from anthropogenic open area data sets
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Cited by 3 publications
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This research focuses on the analytic hierarchy model in the decision-making system that has a more complex structure and maintains the stability of the system, models the application process with the complexity and diversity of the rural economy, collects sample data with the help of different types of rural tourism questionnaire surveys, and integrates the data of rural tourism and other tourism into the model. The following are obtained: (1) During the level analysis, each phenotype track uses RRM, C 1 = 0.26 , C 2 = 0.223 , C 3 = 0.52 , C 4 = 0.25 , C 5 = 0.833 , C 6 = 0.442 , C 7 = 0.75 , C 8 = 0.127 , C 9 = 0.876 , C 10 = 0.792 , C 11 = 0.049 , C 12 = 0.16 , C 13 = 0.166 , and C 14 = 0.049 . The problems of the complex structure of the evaluation can be divided into simple analysis modules, and each module is analyzed at a level. The phenotypic trajectory of each individual is divided into target layer, standard layer, and scheme layer. (2) Arrangement and decision modeling were performed according to one or several indicators of different factors. In the hierarchical random regression model, APC = 0.214 , UPUA = 0.042 , TO = 0.081 , YPUA = 0.082 , PCP = 0.068 , and APS = 0.067 . The characteristic quantity analysis of different environments can be carried out, and the amplitude error and frequency error obtained are relatively small. IAND = 0.115 , AVA = 0.198 , RD = 0.119 , PI = 0.041 , PCCL = 0.142 , IOC = 0.201 , and DSTC = 0.069 . The comparison shows that the hierarchical analysis model is better than the hierarchical random regression model. (3) High-efficiency hybrid model correlation acceleration is the worst model. The experimental data are APC = 0.147 , UPUA = 0.029 , TO = 0.055 , YPUA = 0.06 , PCP = 0.047 , APS = 0.046 , IAND = 0.079 , AVA = 0.136 , RD = 0.082 , PI = 0.028 , PCCL = 0.098 , IOC = 0.139 , and DSTC = 0.048 . (4) The predicted 2020 data and the actual data have small errors. The data obtained by the AHP model is GDP = 1262.1 , finance = 185.09 , budget = 68 , tax = 51.92 , fund budget = 69.23 , transfer income = 40.14 , debt income = 7.73 , disposable financial power = 177.37 , fiscal expenditure = 191.26 , public budget = 88.68 , government expenditure = 71.39 , transfer expenditure = 23.46 , debt expenditure = 7.73 , and last year balance = 2.39 .
This research focuses on the analytic hierarchy model in the decision-making system that has a more complex structure and maintains the stability of the system, models the application process with the complexity and diversity of the rural economy, collects sample data with the help of different types of rural tourism questionnaire surveys, and integrates the data of rural tourism and other tourism into the model. The following are obtained: (1) During the level analysis, each phenotype track uses RRM, C 1 = 0.26 , C 2 = 0.223 , C 3 = 0.52 , C 4 = 0.25 , C 5 = 0.833 , C 6 = 0.442 , C 7 = 0.75 , C 8 = 0.127 , C 9 = 0.876 , C 10 = 0.792 , C 11 = 0.049 , C 12 = 0.16 , C 13 = 0.166 , and C 14 = 0.049 . The problems of the complex structure of the evaluation can be divided into simple analysis modules, and each module is analyzed at a level. The phenotypic trajectory of each individual is divided into target layer, standard layer, and scheme layer. (2) Arrangement and decision modeling were performed according to one or several indicators of different factors. In the hierarchical random regression model, APC = 0.214 , UPUA = 0.042 , TO = 0.081 , YPUA = 0.082 , PCP = 0.068 , and APS = 0.067 . The characteristic quantity analysis of different environments can be carried out, and the amplitude error and frequency error obtained are relatively small. IAND = 0.115 , AVA = 0.198 , RD = 0.119 , PI = 0.041 , PCCL = 0.142 , IOC = 0.201 , and DSTC = 0.069 . The comparison shows that the hierarchical analysis model is better than the hierarchical random regression model. (3) High-efficiency hybrid model correlation acceleration is the worst model. The experimental data are APC = 0.147 , UPUA = 0.029 , TO = 0.055 , YPUA = 0.06 , PCP = 0.047 , APS = 0.046 , IAND = 0.079 , AVA = 0.136 , RD = 0.082 , PI = 0.028 , PCCL = 0.098 , IOC = 0.139 , and DSTC = 0.048 . (4) The predicted 2020 data and the actual data have small errors. The data obtained by the AHP model is GDP = 1262.1 , finance = 185.09 , budget = 68 , tax = 51.92 , fund budget = 69.23 , transfer income = 40.14 , debt income = 7.73 , disposable financial power = 177.37 , fiscal expenditure = 191.26 , public budget = 88.68 , government expenditure = 71.39 , transfer expenditure = 23.46 , debt expenditure = 7.73 , and last year balance = 2.39 .
Immobilization of lipases from the fungus Metarhizium anisopliae ICBC 425 using babassu nut bagasse as substrate on cellulose fibers from lulo stalks
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Growth and population structure of Lodoicea maldivica in natural stands in Seychelles
We monitored leaf production in seedlings, trunkless juvenile, immature, and mature male and female plants of the dioecious palm, Lodoicea maldivica, and studied how internode length changed with trunk height. The fieldwork was conducted in closed forest on Praslin Island and degraded forest on Curieuse Island. Data on numbers of leaves produced and rates of leaf production were used to estimate plant age. On Praslin, the interval between successive leaves increased from 0.47/0.52 years in male/female plants to 4.2 years in seedlings, and on Curieuse from 0.41/0.49 to 2.3 years. Estimated leaf lifespan was 6.4–6.8 years in mature palms and much longer in seedlings and juveniles. On Praslin, internode length increased from the base of the trunk to a mean of 14 cm at leaf 21, before declining to 2.75 cm above leaf 100. Mean internode length of the smaller palms on Curieuse was 1.9 cm and varied little with height. Plants at the same development stage varied widely in age. On Praslin, median time to maturity was 77 (range: 32–209) and on Curieuse 83 (31–191) years. The tallest palms on Praslin (28.4 m trunk height) and Curieuse (8 m) were estimated at 442 and 232 years old, respectively. The ageing method was used to interpret height data of different populations. All showed a marked decline in regeneration in the 19th or early 20th centuries, probably caused by fires. We conclude that slow growth makes this species very vulnerable to disturbance, especially from fire.
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