The network social support experience of people involved in home care

Objective: To build a theoretical model to configure the network social support experience of people involved in home care. Method: A quantitative approach research, utilizing the Grounded Theory method. The simultaneous data collection and analysis allowed the interpretation of the phenomenon meaning The network social support of people involved in home care. Results: The population passive posture in building their well-being was highlighted. The need of a shared responsibility between the involved parts, population and State is recognized. Conclusion: It is suggested for nurses to be stimulated to amplify home care to attend the demands of caregivers; and to elaborate new studies with different populations, to validate or complement the proposed theoretical model.


Social network of family caregivers of disabled and dependent patients

Cross-sectional study that used the Social Network Index and the genogram to assess the social network of 110 family caregivers of dependent patients attended by a Home Care Service in São Paulo, Brazil. Data were analyzed using the test U of Mann-Whitney, Kruskal-Wallis and Spearman correlation. Results were considered statistically significant when p<0,05. Few caregivers participated in activities outside the home and the average number of people they had a bond was 4,4 relatives and 3,6 friends. Caregivers who reported pain and those who had a partner had higher average number of relatives who to trust. The average number of friends was higher in the group that reported use of medication for depression. Total and per capita incomes correlated with the social network. It was found that family members are the primary caregiver’s social network.





Relationships between host plant architecture and gall abundance and survival

The plant architecture hypothesis predicts that variation in host plant architecture influences insect herbivore community structure, dynamics and performance. In this study we evaluated the effects of Macairea radula (Melastomataceae) architecture on the abundance of galls induced by a moth (Lepidoptera: Gelechiidae). Plant architecture and gall abundance were directly recorded on 58 arbitrarily chosen M. radula host plants in the rainy season of 2006 in an area of Cerrado vegetation, southeastern Brazil. Plant height, dry biomass, number of branches, number of shoots and leaf abundance were used as predicting variables of gall abundance and larval survival. Gall abundance correlated positively with host plant biomass and branch number. Otherwise, no correlation (p > 0.05) was found between gall abundance with shoot number or with the number of leaves/plant. From a total of 124 galls analyzed, 67.7% survived, 14.5% were attacked by parasitoids, while 17.7% died due to unknown causes. Larvae that survived or were parasitized were not influenced by architectural complexity of the host plant. Our results partially corroborate the plant architecture hypothesis, but since parasitism was not related to plant architecture it is argued that bottom-up effects may be more important than top-down effects in controlling the population dynamics of the galling lepidopteran. Because galling insects often decrease plant fitness, the potential of galling insects in selecting for less architectural complex plants is discussed.

Soil infiltration based on bp neural network and grey relational analysis

Soil infiltration is a key link of the natural water cycle process. Studies on soil permeability are conducive for water resources assessment and estimation, runoff regulation and management, soil erosion modeling, nonpoint and point source pollution of farmland, among other aspects. The unequal influence of rainfall duration, rainfall intensity, antecedent soil moisture, vegetation cover, vegetation type, and slope gradient on soil cumulative infiltration was studied under simulated rainfall and different underlying surfaces. We established a six factor-model of soil cumulative infiltration by the improved back propagation (BP)-based artificial neural network algorithm with a momentum term and self-adjusting learning rate. Compared to the multiple nonlinear regression method, the stability and accuracy of the improved BP algorithm was better. Based on the improved BP model, the sensitive index of these six factors on soil cumulative infiltration was investigated. Secondly, the grey relational analysis method was used to individually study grey correlations among these six factors and soil cumulative infiltration. The results of the two methods were very similar. Rainfall duration was the most influential factor, followed by vegetation cover, vegetation type, rainfall intensity and antecedent soil moisture. The effect of slope gradient on soil cumulative infiltration was not significant.