Factors related to satisfaction with community-based home aging services in Shandong, China

PurposeThis study investigated the satisfaction of current community-based home care services and its factors in adults aged ≥60 years.MethodsUsing stratified cluster random sampling, we surveyed 1,494 older adults in Jinan and Qingdao, Shandong province, between 2021 and 2023. The baseline and satisfaction surveys were designed by our research team, and the questionnaires were conducted in the form of structured interviews. Kruskal-Wallis H-test and Logistic regression analysis were used to explore the influencing factors of satisfaction.ResultsThe satisfaction was mainly affected by age (p = 0.007), marital status (p < 0.001), pre-retirement occupation (p = 0.003), economic source (p < 0.001), and mode of residence (p = 0.001) in the study of 1,494 older adults. Under the influence of multiple factors, the evaluation of older adults services, married [OR = 4.039, 95% CI: 1.176–13.877] were more inclined to be average, and their occupations were agriculture, forestry, animal husbandry, fishery, and water production workers [OR = 0. 237, 95% CI: 0.068–0.819] and production and transportation equipment operators and related personnel [OR = 0.153, 95% CI: 0.024–0.966] or [OR = 0.153, 95% CI: 0.029–0.820] tended to be more dissatisfied.ConclusionThe satisfaction level of community-based home care services is relatively high among older adults, and it is mainly affected by factors such as age, marital status, pre-retirement occupation, source of financial resources, and mode of residence. Addressing the emotional needs of older adults, lowering the cost of aging, and integrating health care and aging seamlessly are among the ongoing challenges that we need to tackle

Ghrelin attenuates avascular necrosis of the femoral head induced by steroids in rabbits

Purpose: Ghrelin is an endogenous ligand for growth hormone secretagogue receptor. The current study was aimed at examining the effect of ghrelin on avascular necrosis of the femoral head (ANFH) induced by steroids in a rabbit model and also exploring the underlying mechanism. Methods: Experimental rabbits were separated into three groups: Control, Vehicle and Ghrelin. We established a steroid-induced ANFH model in rabbits. Then, MRI scanning and hematoxylin-eosin staining (HE) were conducted to see ANFH. The mRNA levels of Vascular Endothelial Growth Factor (VEGF) and Bone Morphogenetic Protein 2 (BMP-2) were evaluated using real-time qRT-PCR. Results: Rabbits in the Vehicle group showed increased empty bone lacunae, reduced bone trabecula in femoral head; the number of hematopoietic cells in the bone marrow was reduced, whereas number of adipocytes increased with evident fusion phenomenon in comparison with the Control group. All of the changes induced in Vehicle group were attenuated in Ghrelin group. MRI scanning showed obvious necrosis of femoral head in the Vehicle group and less in the Ghrelin group. The mRNA levels of VEGF and BMP-2 were raised in Vehicle group and further enhanced in Ghrelin group. Conclusion: Ghrelin attenuates steroid-induced avascular necrosis in femoral head in rabbit model. A possible mechanism may be through VEGF/BMP-2 axis. Keywords: ANFH, BMP-2, Ghrelin, VEG

Effects of the Spatial Resolution of UAV Images on the Prediction and Transferability of Nitrogen Content Model for Winter Wheat

UAV imaging provides an efficient and non-destructive tool for characterizing farm information, but the quality of the UAV model is often affected by the image’s spatial resolution. In this paper, the predictability of models established using UAV multispectral images with different spatial resolutions for nitrogen content of winter wheat was evaluated during the critical growth stages of winter wheat over the period 2021–2022. Feature selection based on UAV image reflectance, vegetation indices, and texture was conducted using the competitive adaptive reweighted sampling, and the random forest machine learning method was used to construct the prediction model with the optimized features. Results showed that model performance increased with decreasing image spatial resolution with a R2, a RMSE, a MAE and a RPD of 0.84, 4.57 g m−2, 2.50 g m−2 and 2.34 (0.01 m spatial resolution image), 0.86, 4.15 g m−2, 2.82 g m−2 and 2.65 (0.02 m), and 0.92, 3.17 g m−2, 2.45 g m−2 and 2.86 (0.05 m), respectively. Further, the transferability of models differed when applied to images with coarser (upscaling) or finer (downscaling) resolutions. For upscaling, the model established with the 0.01 m images had a R2 of 0.84 and 0.89 when applied to images with 0.02 m and 0.05 m resolutions, respectively. For downscaling, the model established with the 0.05 m image features had a R2 of 0.86 and 0.83 when applied to images of 0.01 m and 0.02 m resolutions. Though the image spatial resolution affects image texture features more than the spectral features and the effects of image spatial resolution on model performance and transferability decrease with increasing plant wetness under irrigation treatment, it can be concluded that all the UAV images acquired in this study with different resolutions could achieve good predictions and transferability of the nitrogen content of winter wheat plants

Effects of the Spatial Resolution of UAV Images on the Prediction and Transferability of Nitrogen Content Model for Winter Wheat

UAV imaging provides an efficient and non-destructive tool for characterizing farm information, but the quality of the UAV model is often affected by the image’s spatial resolution. In this paper, the predictability of models established using UAV multispectral images with different spatial resolutions for nitrogen content of winter wheat was evaluated during the critical growth stages of winter wheat over the period 2021–2022. Feature selection based on UAV image reflectance, vegetation indices, and texture was conducted using the competitive adaptive reweighted sampling, and the random forest machine learning method was used to construct the prediction model with the optimized features. Results showed that model performance increased with decreasing image spatial resolution with a R2, a RMSE, a MAE and a RPD of 0.84, 4.57 g m−2, 2.50 g m−2 and 2.34 (0.01 m spatial resolution image), 0.86, 4.15 g m−2, 2.82 g m−2 and 2.65 (0.02 m), and 0.92, 3.17 g m−2, 2.45 g m−2 and 2.86 (0.05 m), respectively. Further, the transferability of models differed when applied to images with coarser (upscaling) or finer (downscaling) resolutions. For upscaling, the model established with the 0.01 m images had a R2 of 0.84 and 0.89 when applied to images with 0.02 m and 0.05 m resolutions, respectively. For downscaling, the model established with the 0.05 m image features had a R2 of 0.86 and 0.83 when applied to images of 0.01 m and 0.02 m resolutions. Though the image spatial resolution affects image texture features more than the spectral features and the effects of image spatial resolution on model performance and transferability decrease with increasing plant wetness under irrigation treatment, it can be concluded that all the UAV images acquired in this study with different resolutions could achieve good predictions and transferability of the nitrogen content of winter wheat plants

Where is tea grown in the world: a robust mapping framework for agroforestry crop with knowledge graph and sentinels images

Here we present the first 10-m updated tea plantation map over conterminous China in 2022 by fusing S1 and S2 time series. The proposed tea mapping framework could be implemented to generate multiple-year products in China and other countries with tea plantations.</p