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ObjectivesThis study aims to explore the potential mediating role of resilience between care burden and depressive symptoms in family caregivers of stroke patients.MethodsA cross-sectional study was conducted with a convenience sample involving 245 main family caregivers of stroke patients recruited from the neurology department of a Tertiary A hospital in China. Mediation analyses were conducted using the PROCESS macro (Model 4) for SPSS, applying the Bootstrap analysis with 5,000 samples and a 95% confidence interval.ResultsThe results showed that with constant hemiplegia side, Barthel Index, education level, monthly income, care time per day, and living with patients in regression equations, the resilience partially mediated the correlation of care burden and depressive symptoms with a mediation effect ratio of 26.32%.ConclusionsResilience plays a mediating role in the correlation between care burden and depressive symptoms.ImpactThe findings indicated a protective effect of resilience in alleviating the negative influences of care burden on depressive symptoms, suggesting that resilience-training intervention may be developed to mitigate depressive symptoms of the main family caregivers of stroke patients.

Timely crop disease detection, pathogen identification, and infestation severity assessments can aid disease prevention and control efforts to mitigate crop-yield decline. However, improved disease monitoring methods are needed that can extract high-resolution, accurate, and rich color and spatial features from leaf disease spots in the field to achieve precise fine-grained disease-severity classification and sensitive disease-recognition accuracy. Here, we propose a neural-network-based method incorporating an improved Rouse spatial pyramid pooling strategy to achieve crop disease detection against a complex background. For neural network construction, first, a dual-attention module was introduced into the cross-stage partial network backbone to enable extraction of multi-dimensional disease information from the channel and space perspectives. Next, a dilated convolution-based spatial pyramid pooling module was integrated within the network to broaden the scope of the collection of crop-disease-related information from images of crops in the field. The neural network was tested using a set of sample data constructed from images collected at a rate of 40 frames per second that occupied only 17.12 MB of storage space. Field data analysis conducted using the miniaturized model revealed an average precision rate approaching 90.15% that exceeded the corresponding rates obtained using comparable conventional methods. Collectively, these results indicate that the proposed neural network model simplified disease-recognition tasks and suppressed noise transmission to achieve a greater accuracy rate than is obtainable using similar conventional methods, thus demonstrating that the proposed method should be suitable for use in practical applications related to crop disease recognition.