Objective: Turnover intention of employees in elderly caring social organizations has a significant impact on elderly care service delivery. This study investigated the associated factors of turnover intention among employees of elderly caring social organizations in Anhui Province, China.Methods: A total of 605 participants were selected using a multi-stage stratified random sampling method. A self-administered questionnaire was used to collect information on socio-demographic, social support, and turnover intention from the participants. The data was analyzed through descriptive statistical analysis, one-way variance analysis, Spearman correlation analysis, and multiple linear regression were used to analyze the factors related to turnover intention.Results: Results of our study showed that the total score of turnover intention, turnover intention I (possibility of quitting a current job),turnover intention II (motivation to find other jobs) and turnover intention III (obtaining the external possibility of work) were 8.84, 2.32, 2.38, and 4.14, respectively. Social support negatively correlated with turnover intention I and turnover intention II. However, it showed positive correlation with turnover intention III and total turnover intention scores; turnover intentionI (coefficient: − 0.082), turnover intention II (coefficient: − 0.071), turnover intention III (coefficient: 0.19), Total score of turnover intention (coefficient: 0.093). Ethnic group, age, education level, and job satisfaction were associated with turnover intention.Conclusion: Improvement of social support play an important role in reducing the turnover intention of employees in elderly caring social organizations. It is important to increase organizational commitment and strengthen psychological empowerment, combined with decreasing job burnout for stability.

A plant factory with artificial lighting (PFAL) usually uses top lighting for cultivation. The light from the upper part of the canopy cannot penetrate the entire lettuce canopy, however, resulting in uneven vertical spatial light in the canopy, and accelerating the senescence of both the bottom and side leaves of the plant canopy. Therefore, in this study, the performance of lettuce in hydroponics was investigated upon supplemental side lighting with different spectral LEDs in a PFAL. A set of short-term side lighting treatments, including no side lamps (CK), red (R), blue (B), red + blue (RB), and red + blue + green (RGB) LED lamps (150 μmol·m⁻²·s⁻¹, respectively), was employed for an additional 2 h per day after normal top lighting for 6 days before harvest. The results showed that the lettuce canopy was relatively loose and had a large crown size under side lighting compared with CK. Side lighting, irrespective of spectral qualities, significantly increased the fresh weight, and the R, B, RB, and RGB treatments increased the shoot fresh weight of lettuce plants by 34%, 19%, 31%, and 34%, and increased the fresh weight of leaf layer 2 by 50%, 17%, 44%, and 48%, respectively. The side lighting of different spectral qualities had a significant impact on the nutritional quality of the first row of lettuce at the edge of the top lighting illuminated area. Treatment B significantly promoted the chlorophyll content of leaf layer 3; the soluble sugar contents from leaf layer 1, 2, and 3; the starch contents in leaf layers 2 and 3; and the content of phenolics in the leaf layers 3; and significantly reduced the nitrate content in leaf layers 2 and 3. RGB significantly increased soluble sugar content by 91%, and the starch content in leaf layer 1, as well as the leaf chlorophyll and flavonoid content of leaf layer 3, while R had opposite effect completely. RB significantly increased the leaf chlorophyll content of leaf layer 3 and the nitrate content in leaf layer 1, but the overall effect was lower than that of RGB. In summary, side lighting of any type could effectively improve lettuce yield, solve the problem of inconsistent lettuce plant size caused by the edge effect of top lighting, and affect the nutritional quality of lettuce. B and RGB performed best. There was spatial response diversity of lettuce plants to side lighting spectral qualities.

Alfalfa is a widely grown forage with a high crude protein content. Clarifying the interactions between light quality and nitrogen level on yield and nitrogen metabolism can purposely improve alfalfa productivity in plant factories with artificial light (PFAL). In this study, the growth, quality, and nitrogen metabolism of alfalfa grown in PFAL were investigated using three nitrate-nitrogen concentrations (10, 15, and 20 mM, labeled as N₁₀, N₁₅, and N₂₀) and continuous light (CL) with two light qualities (red–blue and red–blue–green light, labeled as RB-C and RBG-C). The results showed that the adaptation performance of alfalfa to nitrogen concentrations differed under red–blue and red–blue–green CL. Plant height, stem diameter, leaf area, yield, Chl a + b, Chl a, Chl b, crude protein contents, and NiR activity under the RB-CN₁₅ treatment were significantly higher than RB-CN₁₀ and RB-CN₂₀ treatments. The RB-CN₂₀ treatment showed morphological damage, such as plant dwarfing and leaf chlorosis, and physiological damage, including the accumulation of proline, H₂O₂, and MDA. However, the difference was that under red–blue–green CL, the leaf area, yield, and Chl a + b, carotenoid, nitrate, and glutamate contents under RBG-CN₂₀ treatment were significantly higher than in the RBG-CN₁₀ and RBG-CN₁₅ treatments. Meanwhile, the contents of soluble sugar, starch, and cysteine were significantly lower. However, the crude protein content reached 21.15 mg·g⁻¹. The fresh yield, dry yield, stomatal conductance, leaf area, plant height, stem diameter, crude protein, GS, and free amino acids of alfalfa were positively correlated with increased green light. In addition, with the increase in nitrogen concentration, photosynthetic capacity, NiR, and GOGAT activities increased, promoting growth and improving feeding value. The growth, yield, photosynthetic pigments, carbon, nitrogen substances, and enzyme activities of alfalfa were significantly affected by the interaction between nitrogen concentration and light quality, whereas leaf/stem ratio and DPPH had no effect. In conclusion, RB-CN15 and RBG-CN₂₀ are suitable for the production of alfalfa in PFAL, and green light can increase the threshold for the nitrogen concentration adaptation of alfalfa.

With the rapid advancements in computer vision and deep learning, multi-view stereo (MVS) based on conventional RGB cameras has emerged as a promising and cost-effective tool for botanical research. However, existing methods often struggle to capture the intricate textures and fine edges of plants, resulting in suboptimal 3D reconstruction accuracy. To overcome this challenge, we proposed Edge_MVSFormer on the basis of TransMVSNet, which particularly focuses on enhancing the accuracy of plant leaf edge reconstruction. This model integrates an edge detection algorithm to augment edge information as input to the network and introduces an edge-aware loss function to focus the network’s attention on a more accurate reconstruction of edge regions, where depth estimation errors are obviously more significant. Edge_MVSFormer was pre-trained on two public MVS datasets and fine-tuned with our private data of 10 model plants collected for this study. Experimental results on 10 test model plants demonstrated that for depth images, the proposed algorithm reduces the edge error and overall reconstruction error by 2.20 ± 0.36 mm and 0.46 ± 0.07 mm, respectively. For point clouds, the edge and overall reconstruction errors were reduced by 0.13 ± 0.02 mm and 0.05 ± 0.02 mm, respectively. This study underscores the critical role of edge information in the precise reconstruction of plant MVS data.