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Background The weight-adjusted waist index (WWI) is a quantitative anthropometric index that can be applied to evaluate obesity. This study examined the relationship between adult United States (US) residents’ risk of diabetes mellitus type 2 (T2DM) and WWI. Methods The NHANES (National Health and Nutrition Examination Survey) 2001–2018 provided the data for this investigation. This study used multifactorial logistic regression analysis, smoothed curve fitting, subgroup analysis, and interaction tests to assess the association between WWI and T2DM. Additionally, threshold effects were calculated using a two-stage linear regression model. The receiver operating characteristic(ROC) curves evaluated the diagnostic ability of the WWI and commonly used obesity indicators. Results 20,477 participants were enrolled in the analysis, and patients with greater levels of WWI had a higher prevalence of T2DM. WWI and T2DM have a non-linear relationship, with a positive association found on the left side of the breakpoint (WWI = 12.35) (OR = 1.82, 95%CI:1.64–2.02), whereas, on the right side, no such relationship was found (OR = 0.9, 95%CI:0.61–1.34). For every unit rise in WWI, the probability of having T2DM increased by 67% after controlling for all other variables (OR:1.67,95%CI:1.53–1.83). Based on subgroup analyses, individuals under 40 had a higher correlation between WWI and T2DM (P  < 0.001).ROC analyses showed that WWI had the best discrimination and accuracy in predicting T2DM compared to other obesity indicators (WC, BMI, and Weight). Conclusion Higher WWI values had a higher prevalence of T2DM in US individuals, especially in adults under 40. WWI has the strongest ability to predict T2DM. Therefore, the importance of WWI in the early identification of T2DM in US adults should be emphasized.

In order to solve the influence of the complex interaction relationships among subjects on the system solution accuracy and speed of the Multi-Microgrid system under the high penetration rate of new energy. Firstly, the paper establishes the bi-level optimal scheduling Stackelberg game model based on shared energy storage, considering the inter-subject interaction in MMG. Subsequently, based on the four improvement methods of Chaotic Map, Quantum Behavior, Gaussian Distribution, and Nonlinear Control Strategy, the Chaotic Gaussian Quantum Crayfish Optimization Algorithm is proposed to solve the optimization scheduling model. The improved algorithm exhibits superior initial solutions and enhanced search capability. In comparison to the original algorithm, the relative errors of the CGQCOA optimization outcomes are 98%, 20.96%, 98.74% and 16.55%, respectively, enhancing the model-solving accuracy and the speed of convergence to the optimal solution. Finally, the simulation demonstrates that the revenue of Microgrid 1, Microgrid 2, and Microgrid 3 have increased by 0.73%, 1.17%, and 1.04%, respectively. Concurrently, the penalty cost of pollutant emission has decreased by 5.9%, 11.5%, and 12.68%, respectively. Furthermore, the revenue of the shared storage have increased by 1.91%. These findings validate the efficacy of the methodology proposed in enhancing the revenue of the various subjects and reducing pollutant gas emission.

This article examines everyday feminism on RedNote (Xiaohongshu) through the lens of affective politics, exploring how feminists repurpose a consumerist digital platform to cultivate feminist publics within a socio-political environment that is largely unreceptive. The analysis reveals that mediated intimacy underpins the everyday practices of RedNote feminists, facilitating the formation of intimate publics. These practices strategically engage the platform’s vernacular to articulate a “lifestyle feminism,” framing feminist politics as a non-confrontational, identity-based lifestyle rather than an overt ideological stance. While this approach nurtures a popularized feminist enclave, it is accompanied by inherent tensions: limited intersectionality, insularity, and susceptibility to platform co-optation may transform this ostensibly empowering mode of feminist politics into its opposite, inadvertently contributing to the depoliticization and censorship it seeks to resist.

Menthol in mints elicits coolness sensation by selectively activating TRPM8 channel. Although structures of TRPM8 were determined in the apo and liganded states, the menthol-bounded state is unresolved. To understand how menthol activates the channel, we docked menthol to the channel and systematically validated our menthol binding models with thermodynamic mutant cycle analysis. We observed that menthol uses its hydroxyl group as a hand to specifically grab with R842, and its isopropyl group as legs to stand on I846 and L843. By imaging with fluorescent unnatural amino acid, we found that menthol binding induces wide-spread conformational rearrangements within the transmembrane domains. By Φ analysis based on single-channel recordings, we observed a temporal sequence of conformational changes in the S6 bundle crossing and the selectivity filter leading to channel activation. Therefore, our study suggested a ‘grab and stand’ mechanism of menthol binding and how menthol activates TRPM8 at the atomic level. Menthol in mints elicits a coolness sensation by selective activation of TRPM8 ion channel. Here authors dock menthol to TRPM8 and systematically validate their menthol binding models with thermodynamic mutant cycle analysis in functional tests, and shed light on TRPM8 activation by menthol at the atomic level.

Background Due to the high cost of data collection for magnetization detection of media, the sample size is limited, it is not suitable to use deep learning method to predict its change trend. The prediction of physical and chemical properties of magnetized water and fertilizer (PCPMWF) by meta-learning can help to explore the effects of magnetized water and fertilizer irrigation on crops. Method In this article, we propose a meta-learning optimization model based on the meta-learner LSTM in the field of regression prediction of PCPMWF. In meta-learning, LSTM is used to replace MAML’s gradient descent optimizer for regression tasks, enables the meta-learner to learn the update rules of the LSTM, and apply it to update the parameters of the model. The proposed method is compared with the experimental results of MAML and LSTM to verify the feasibility and correctness. Results The average absolute percentage error of the meta-learning optimization model of meta-learner LSTM is reduced by 0.37% compared with the MAML model, and by 4.16% compared with the LSTM model. The loss value of the meta-learning optimization model in the iterative process drops the fastest and steadily compared to the MAML model and the LSTM model. In cross-domain experiments, the average accuracy of the meta-learning optimized model can still reach 0.833. Conclusions In the case of few sample, the proposed model is superior to the traditional LSTM model and the basic MAML model. And in the training of cross-domain datasets, this model performs best.

For a class of continuous-time nonlinear systems with input constraints, a novel event triggered control (ETC) of integral reinforcement learning (IRL) based on sliding mode (SM) is proposed in this paper. Firstly, a SM surface-based performance index function is designed and the Hamiltonian equation is solved by the policy iteration algorithm. Secondly, the IRL technique is utilized to obtain the integral Bellman equation, which makes the controller do not need to know the drift dynamics. Thirdly, the ETC is introduced to reduce the communication burden and a triggering condition is designed to ensure the asymptotic stability of the system. Then, a critic neural network (NN) is used to learn the optimal value function to obtain the optimal tracking controller. Finally, the asymptotic stability of the whole closed-loop system and uniformly ultimately bounded of the critic NN weights are proved based on the Lyapunov theory. Simulation and comparison results demonstrate the effectiveness of the proposed method.

The atmospheric temperature profile is an important parameter to describe the state of the atmosphere, and it is crucial to climate change research, weather forecasting, and atmospheric parameter retrieval. A machine learning algorithm that incorporates historical observations and ground-based measurements was developed in this study to retrieve the atmospheric temperature profile. Specifically, the deep learning network considered historical observations for the same period and temporally correlated temperature profiles. It combined multi-layer perceptron (MLP) and the convolutional neural network (CNN). MLP derived the features from the ground factors, and CNN captured the essential features associated with the temperature profiles at the current time from latent historical data. Then, the features of the two parts were concatenated to obtain the final network. The construction and parameters of the model were optimized to determine the best model configuration and retrieval performance. The results of the model were evaluated against those of other methods on the same dataset. The model showed a good retrieval precision, which was equivalent to a small retrieval bias, root-mean-square error, and mean absolute error at all altitudes. The analysis of the application of this model to the retrieval of atmospheric temperature profiles indicates that the method is feasible.

In recent years, with the rapid development of artificial intelligence technology, computer vision-based pest detection technology has been widely used in agricultural production. Tomato diseases and pests are serious problems affecting tomato yield and quality, so it is important to detect them quickly and accurately. In this paper, we propose a tomato disease and pest detection model based on an improved YOLOv5n to overcome the problems of low accuracy and large model size in traditional pest detection methods. Firstly, we use the Efficient Vision Transformer as the feature extraction backbone network to reduce model parameters and computational complexity while improving detection accuracy, thus solving the problems of poor real-time performance and model deployment. Second, we replace the original nearest neighbor interpolation upsampling module with the lightweight general-purpose upsampling operator Content-Aware ReAssembly of FEatures to reduce feature information loss during upsampling. Finally, we use Wise-IoU instead of the original CIoU as the regression loss function of the target bounding box to improve the regression prediction accuracy of the predicted bounding box while accelerating the convergence speed of the regression loss function. We perform statistical analysis on the experimental results of tomato diseases and pests under data augmentation conditions. The results show that the improved algorithm improves mAP50 and mAP50:95 by 2.3% and 1.7%, respectively, while reducing the number of model parameters by 0.4 M and the computational complexity by 0.9 GFLOPs. The improved model has a parameter count of only 1.6 M and a computational complexity of only 3.3 GFLOPs, demonstrating a certain advantage over other mainstream object detection algorithms in terms of detection accuracy, model parameter count, and computational complexity. The experimental results show that this method is suitable for the early detection of tomato diseases and pests.

Mesenchymal stem cells (MSCs) are multipotent stem cells characterized by their robust proliferative capacity, homing ability, differentiation potential, and low immunogenicity in vitro. MSCs can be isolated from a variety of tissues, primarily including but not limited to bone marrow, adipose tissue, umbilical cord, placenta, and dental pulp. Although there have been a large number of clinical studies on the treatment of diseases by MSCs and MSCs-derived exosomes (MSCs-EXO), the large-scale clinical application of MSCs and MSCs-EXO have been limited due to the heterogeneity of the results among various studies. This review provides a detailed description of the classification and characterization of MSCs and MSCs-EXO, as well as their extraction methods. Furthermore, this review elaborates on three key mechanisms of MSCs and MSCs-EXO: paracrine mechanisms, immunomodulatory and anti-inflammatory effects, as well as their promotion of tissue regeneration. This review also examines the role of MSCs and MSCs-EXO in cardiovascular diseases, neurological disorders, autoimmune diseases, musculoskeletal disorders, and other systemic diseases over the past five years, while discussing the challenges and difficulties associated with their clinical application. Finally, we systematically summarized and analyzed the potential causes of the various heterogeneous results currently observed. Additionally, we provided an in-depth discussion on the challenges and opportunities associated with the clinical translation of disease treatment approaches based on MSCs, MSCs-EXO, and engineered exosomes.

Accurate and efficient individual tree species (ITS) classification is the basis of fine forest resource management. It is a challenge to classify individual tree species in dense forests using remote sensing imagery. In order to solve this problem, a new ITS classification method was proposed in this study, in which a hierarchical convolutional neural network (H-CNN) model and multi-temporal high-resolution Google Earth images were employed. In an experiment conducted in a forest park in Beijing, China, GE images of several significant phenological phases of broad-leaved forests, namely, before and after the mushrooming period, the growth period, and the wilting period, were selected, and ITS classifications based on these images along with several typical CNN models and the H-CNN model were conducted. In the experiment, the classification accuracy of the multitemporal images was higher by 7.08–12.09% than those of the single-temporal images, and the H-CNN model offered an OA accuracy 2.66–3.72% higher than individual CNN models, demonstrating that multitemporal images rich in the phenological features of individual tree species, together with a hierarchical CNN model, can effectively improve ITS classification.