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(1) Background: Macrosomia is prevalent in China and worldwide. The current method of predicting macrosomia is ultrasonography. We aimed to develop new predictive models for recognizing macrosomia using a random forest model to improve the sensitivity and specificity of macrosomia prediction; (2) Methods: Based on the Shandong Multi-Center Healthcare Big Data Platform, we collected the prenatal examination and delivery data from June 2017 to May 2018 in Jinan, including the macrosomia and normal-weight newborns. We constructed a random forest model and a logistic regression model for predicting macrosomia. We compared the validity and predictive value of these two methods and the traditional method; (3) Results: 405 macrosomia cases and 3855 normal-weight newborns fit the selection criteria and 405 pairs of macrosomia and control cases were brought into the random forest model and logistic regression model. On the basis of the average decrease of the Gini coefficient, the order of influencing factors was: interspinal diameter, transverse outlet, intercristal diameter, sacral external diameter, pre-pregnancy body mass index, age, the number of pregnancies, and the parity. The sensitivity, specificity, and area under curve were 91.7%, 91.7%, and 95.3% for the random forest model, and 56.2%, 82.6%, and 72.0% for logistic regression model, respectively; the sensitivity and specificity were 29.6% and 97.5% for the ultrasound; (4) Conclusions: A random forest model based on the maternal information can be used to predict macrosomia accurately during pregnancy, which provides a scientific basis for developing rapid screening and diagnosis tools for macrosomia.

Background The life satisfaction of the elderly in nursing home is the focus of social concern.The purpose of this study was to evaluate the effects of family function and sleep quality on life satisfaction among elderly individuals in nursing homes and examine the mediating effect of sleep quality between family function and life satisfaction. Methods A cross-sectional observational study was conducted .A total of 127 older adults who completed the Life Satisfaction Index A (LSI-A), the Family APGAR Index and the Pittsburgh Sleep Quality Index (PSQI) were recruited from four nursing homes in Chongqing, China. Results Life satisfaction was positively correlated with family function ( r =0.434, p <0.01) and negatively correlated with PSQI ( r = -0.514, p <0.01). PSQI was found to be negatively associated with family function ( r =-0.387, p <0.01).Family function had a significant effect on PSQI (path a: β=-0.8459, 95% CI=-1.2029, -0.4889), and PSQI had a significant effect on life satisfaction (path b: β=-0.3916, 95% CI=-0.5407, -0.2425). The total effect (path c) and direct effect (path c') of family function on life satisfaction were significant (β=0.8931, 95% CI=0.5626, 1.2235 and β=0.56181, 95% CI=0.2358, 0.8879, respectively). The coefficient for the indirect effect of family function on life satisfaction through PSQI was statistically significant (β=0.3312, 95% CI=0.1628, 0.5588). PSQI played a partial mediating role between family function and life satisfaction, and PSQI mediated 32.58% of the total effect of family function on life satisfaction. Conclusions Family function and sleep quality were significant predictors of elderly people's life satisfaction in nursing homes. Sleep quality partially mediated the relationship between family function and life satisfaction.The interventions focused on promoting family function and improving sleep quality may be more helpful in improving elderly people's life satisfaction in nursing homes.

Background Eczema is more prevalent in children aged 0–2 years, yet the long-term effects of air pollutant exposure during early life on the risk of eczema development remain unclear. Methods We conducted a birth cohort study in Jinan, China, to explore the effect of early life air pollutant exposure on the risk of eczema in younger children. An inverse distance weighting method was used for individual exposure assessment. Binary and multivariate logistic models were used to investigate the effects of air pollutants on eczema, the distributed lag model to find critical windows of exposure, weighted quantile sum model and principal component analysis to explore the combined effects of multiple pollutants. Results The cumulative incidence rate for eczema among 5819 children aged 2 was 19.8%. Exposure to high levels of O 3 during pregnancy ( OR 1.12, 95% CI 1.06–1.19) and during the first year after birth ( OR 1.24, 95% CI 1.03–1.50) increased the risk of eczema. PM 2.5−10 during pregnancy, PM 2.5 and PM 2.5−10 during the first year after birth also increased the risk of eczema. The critical window for O 3 and PM exposure was the third trimester and early postnatal period. Moreover, in the joint effect of multiple pollutants, O 3 played a dominant role during pregnancy (weighting > 0.3), with a predominantly O 3 principal component associated with eczema risk (adjusted OR 1.011, 95% CI 1.007–1.015). Conclusions Early-life exposure to O 3 and PM was associated with an increased risk of eczema in children aged 0–2 years, with sensitivity windows appearing to be earlier in life. O 3 exposure during pregnancy played a pivotal role in the combined effects of pollutants on eczema risk.

The accurate detection and monitoring of levee cracks is critical for maintaining the structural integrity and safety of flood protection infrastructure. Yet at present the application of using UAV to achieve an automatic, rapid detection of levee cracks is still limited and there is a lack of effective deblurring methods specifically tailored for UAV-based levee crack images. In this study, we present E2S, a novel two-stage framework specifically designed for UAV-based levee crack segmentation, which leverages an unsupervised deblurring technique to enhance image quality. In the first stage, we introduce an Improved CycleGAN model that mainly performs motion deblurring on UAV-captured images, effectively enhancing crack visibility and preserving crucial structural details. The enhanced images are then fed into the second stage, where an Attention U-Net is employed for precise crack segmentation. The experimental results demonstrate that the E2S framework significantly outperforms traditional supervised models, achieving an F1-score of 81.3% and a crack IoU of 71.84%, surpassing the best-performing baseline, Unet++. The findings confirm that the integration of unsupervised image enhancement can substantially benefit downstream segmentation tasks, providing a robust and scalable solution for automated levee crack monitoring.

Accurate fruit detection under low-visibility conditions such as fog, rain, and low illumination is crucial for intelligent orchard management and robotic harvesting. However, most existing detection models experience significant performance degradation in these visually challenging environments. This study proposes a modular detection framework named Dynamic Coding Network (DCNet), designed specifically for robust fruit detection in low-visibility agricultural scenes. DCNet comprises four main components: a Dynamic Feature Encoder for adaptive multi-scale feature extraction, a Global Attention Gate for contextual modeling, a Cross-Attention Decoder for fine-grained feature reconstruction, and an Iterative Feature Attention mechanism for progressive feature refinement. Experiments on the LVScene4K dataset, which contains multiple fruit categories (grape, kiwifruit, orange, pear, pomelo, persimmon, pumpkin, and tomato) under fog, rain, low light, and occlusion conditions, demonstrate that DCNet achieves 86.5% mean average precision and 84.2% intersection over union. Compared with state-of-the-art methods, DCNet improves F1 by 3.4% and IoU by 4.3%, maintaining a real-time inference speed of 28 FPS on an RTX 3090 GPU. The results indicate that DCNet achieves a superior balance between detection accuracy and computational efficiency, making it well-suited for real-time deployment in agricultural robotics. Its modular architecture also facilitates generalization to other crops and complex agricultural environments.

Urban reservoirs contribute significantly to human survival and ecological balance. Machine learning-based remote sensing techniques for monitoring water quality parameters (WQPs) have gained increasing prominence in recent years. However, these techniques still face challenges such as inadequate band selection, weak machine learning model performance, and the limited retrieval of non-optical active parameters (NOAPs). This study focuses on an urban reservoir, utilizing unmanned aerial vehicle (UAV) multispectral remote sensing and ensemble machine learning (EML) methods to monitor optically active parameters (OAPs, including Chla and SD) and non-optically active parameters (including CODMn, TN, and TP), exploring spatial and temporal variations of WQPs. A framework of Feature Combination and Genetic Algorithm (FC-GA) is developed for feature band selection, along with two frameworks of EML models for WQP estimation. Results indicate FC-GA’s superiority over popular methods such as the Pearson correlation coefficient and recursive feature elimination, achieving higher performance with no multicollinearity between bands. The EML model demonstrates superior estimation capabilities for WQPs like Chla, SD, CODMn, and TP, with an R2 of 0.72–0.86 and an MRE of 7.57–42.06%. Notably, the EML model exhibits greater accuracy in estimating OAPs (MRE ≤ 19.35%) compared to NOAPs (MRE ≤ 42.06%). Furthermore, spatial and temporal distributions of WQPs reveal nitrogen and phosphorus nutrient pollution in the upstream head and downstream tail of the reservoir due to human activities. TP, TN, and Chla are lower in the dry season than in the rainy season, while clarity and CODMn are higher in the dry season than in the rainy season. This study proposes a novel approach to water quality monitoring, aiding in the identification of potential pollution sources and ecological management.

Objectives This study explores the relationships among chronotype, sensation seeking, sleep quality and depressive symptoms in adolescents with diagnosed depression, aiming to clarify the mechanisms by which chronotype is associated with depression. Methods This cross-sectional study assessed 216 adolescents with diagnosed depression using a demographic questionnaire, the Morningness-Eveningness Questionnaire, the Sensation Seeking Scale, the Pittsburgh Sleep Quality Index, and the Beck Depression Inventory. Descriptive and correlational analyses were performed using SPSS 27.0, and structural equation modeling was conducted via AMOS to explore the mediating roles of sensation seeking and sleep quality in the relationship between chronotype and depression. Results The study found that 60.6% of adolescents with depression were evening chronotypes. Evening chronotype was associated with higher sensation seeking ( r  = -0.134, p  < 0.05), poorer sleep quality ( r  = -0.303, p  < 0.01), and more severe depressive symptoms ( r  = -0.376, p  < 0.01). Chronotype showed a direct effect on depressive symptoms (effect size = -0.318, 95% CI = -0.602 to -0.049, p  < 0.05) and an indirect effect via sleep quality, accounting for 80.5% of the total effect. While sensation seeking alone was not a significant mediator, it contributed to a chain mediation with sleep quality, accounting for 13% of the total effect (combined effect size = -0.053, 95% CI = -0.163 to -0.005, p  < 0.01). Conclusions Chronotype may play a significant role in adolescent depression, with both direct and indirect effects mediated by sleep quality and sensation seeking. The findings highlight the potential importance of sleep quality as a mediating factor, indicating that interventions targeting sleep improvement could be a promising avenue for further exploration in alleviating depressive symptoms in adolescents.

Phenomic selection is a new paradigm in plant breeding that uses high‐throughput phenotyping technologies and machine learning models to predict traits of new individuals and make selections. This can allow breeders to evaluate more plants in higher throughput more accurately, resulting in faster rates of gain and reduced labor costs. However, phenomic prediction models are frequently benchmarked against genomic prediction models using cross‐validation to demonstrate their usefulness to breeders. We argue that this is inappropriate for two reasons: (1) differences in the accuracy statistic measured by cross‐validation do not reliably indicate differences in the accuracy parameter of the breeder's equation, which we show analytically and through reanalysis of data from three representative phenomic prediction studies and (2) phenomic and genomic selection tools influence other parameters of the breeder's equation, so comparing accuracy, even if done properly, is insufficient to advocate for one approach over the other. We conclude that phenomic selection may be useful, but comparisons of accuracy between genomic prediction and phenomic prediction models are not. Plain Language Summary Plant breeders use different tools to decide which plants to grow and to make crosses. Two popular tools are genomic prediction, which uses genetic information like DNA, and phenomic prediction, which uses advanced technology like drone images. Several recent papers have claimed phenomic prediction is better at selecting plants than genomic prediction because it is more accurate at predicting plant traits. These papers measured accuracy with predictive ability. However, predictive ability is not a good indicator of accuracy. We re‐analyzed data from 3 published articles and used an appropriate method to measure accuracy. We found that phenomic prediction was not always more accurate than genomic prediction. We explained this using a mathematical proof. Furthermore, we discussed that a more accurate model isn't necessarily a better model. We concluded that phenomic prediction may be useful in some scenarios, but comparing genomic prediction and phenomic prediction accuracy is not.

During accelerated urbanization, the lack of attention to environmental protection and governance led to the formation of black-odor water. The existence of urban black-odor water not only affects the cityscape, but also threatens human health and damages urban ecosystems. The black-odor water bodies are small and hidden, so they require large-scale and high-resolution monitoring which offers a temporal and spatial variation of water quality frequently, and the unmanned aerial vehicle (UAV) with a multispectral instrument is up to the monitoring task. In this paper, the Nemerow comprehensive pollution index (NCPI) was introduced to assess the pollution degree of black-odor water in order to avoid inaccurate identification based on a single water parameter. Based on the UAV-borne multispectral data and NCPI of sampling points, regression models for inverting the parameter indicative of water quality were established using three artificial intelligence algorithms, namely extreme gradient boosting (XGBoost), random forest (RF), and support vector regression (SVR). The result shows that NCPI is qualified to evaluate the pollution level of black-odor water. The XGBoost regression (XGBR) model has the highest fitting accuracy on the training dataset (R2 = 0.99) and test dataset (R2 = 0.94), and it achieved the best retrieval effect on image inversion in the shortest time, which made it the best-fit model compared with the RF regression (RFR) model and the SVR model. According to inversion results based on the XGBR model, there was only a small size of mild black-odor water in the study area, which showed the achievement of water pollution treatment in Guangzhou. The research provides a theoretical framework and technical feasibility for the application of the combination of algorithms and UAV-borne multispectral images in the field of water quality inversion.

The exotic saltmarsh cordgrass, Spartina alterniflora (Loisel) Peterson & Saarela, is one of the important causes for the extensive destruction of mangroves in China due to its invasive nature. The species has rapidly spread wildly across coastal wetlands, challenging resource managers for control of its further spread. An investigation of S. alterniflora invasion and associated ecological risk is urgent in China’s coastal wetlands. In this study, an ecological risk invasive index system was developed based on the Driving Force-Pressure-State-Impact-Response framework. Predictions were made of ‘warning degrees’: zero warning and light, moderate, strong, and extreme warning, by developing a back propagation (BP) artificial neural network model for coastal wetlands in eastern Fujian Province. Our results suggest that S. alterniflora mainly has invaded Kandelia candel beaches and farmlands with clustered distributions. An early warning indicator system assessed the ecological risk of the invasion and showed a ladder-like distribution from high to low extending from the urban area in the central inland region with changes spread to adjacent areas. Areas of light warning and extreme warning accounted for 43% and 7%, respectively, suggesting the BP neural network model is reliable prediction of the ecological risk of S. alterniflora invasion. The model predicts that distribution pattern of this invasive species will change little in the next 10 years. However, the invaded patches will become relatively more concentrated without warning predicted. We suggest that human factors such as land use activities may partially determine changes in warning degree. Our results emphasize that an early warning system for S. alterniflora invasion in China’s eastern coastal wetlands is significant, and comprehensive control measures are needed, particularly for K. candel beach.