Binzhou People's Hospital

Ligated boryl radicals (LBRs), generated from Lewis base-coordinated boranes, have emerged as powerful tools capable of halogen-atom transfer (XAT) processes. Nevertheless, metal-free LBR-enabled radical chemistry remains largely confined to radical additions or cascade pathways. Herein, we report a green-light-induced cyclopropanation of alkenes using dichloromethane (DCM) as a C1 synthon, enabled by the synergistic action of N-heterocyclic nitrenium (NHN) catalysts and LBRs. This work demonstrates a transition-metal-free cyclopropanation of alkenes with DCM, further highlighting the synthetic potential of the NHN/LBR cooperative strategy.

ABSTRACTAims/IntroductionLeukocytes are implicated in the inflammatory cascades of diabetic retinopathy (DR), but their causal roles remain ambiguous. This study employed a two‐sample Mendelian randomization (MR) analysis to dissect the causal effects of circulating leukocyte counts on DR risk.Materials and MethodsWe utilized summary statistics from large‐scale genome‐wide association studies (GWAS) for five leukocyte subtypes and DR in European‐ancestry populations. The inverse‐variance weighted (IVW) method was primary, supported by comprehensive sensitivity analyses including MR‐Egger, weighted median, and the MR‐Pleiotropy Residual Sum and Outlier (MR‐PRESSO) test to ensure result robustness.ResultsA total of 2,136 leukocyte‐related SNPs were extracted as instrumental variables for causal inference. MR analysis revealed that increased lymphocyte counts are associated with reduced DR risk (IVW OR = 0.93, 95% CI = 0.86–0.99, P = 0.03), while the initial association between higher eosinophil counts and DR risk (IVW OR = 1.11, 95% CI = 1.03–1.19, P < 0.01) was attenuated following correction for outliers. No significant associations were observed for basophil, monocyte, or neutrophil counts. Sensitivity analyses found no evidence of pleiotropy or substantial influence from single SNPs.ConclusionsOur findings provide genetic evidence supporting a potential causal association between lymphocyte counts and diabetic retinopathy risk, while the association for eosinophil counts was attenuated after correction for outliers. These results highlight the importance of further investigating the physiological role of lymphocytes in diabetic retinopathy to inform effective prevention and treatment strategies.

ABSTRACT Transition metal oxides (TMOs) such as MoO x , featuring a high work function and a wide optical bandgap, are competitive alternatives to p‐type a‐Si:H or nano crystalline silicon in silicon heterojunction solar cells to form silicon compound heterojunction (SCH) solar cells. However, the thermal instability of MoO x during the curing process of screen‐printing restrains its massive production in industry. In this work, silver grids are printed on the MoO x side of MoO x SCH solar cells, and the influence of annealing atmosphere and temperature on the performance of the solar cells is investigated. After annealing in O 2 or air, the device performance is significantly degraded, while it remains almost unchanged after annealing in N 2 at 136°C. A conversion efficiency of 22.40% is achieved on the SCH solar cells with screen‐printed Ag grids when annealed at 136°C for 40 min in N 2 atmosphere, which is equivalent to that of the solar cells with thermally evaporated grids. To reveal the annealing effect, systematic research is conducted on changes in optoelectronic property, contact resistivity, and compositional distribution of MoO x , brought about by annealing in N 2 , O 2 , and air at different temperatures. Oxygen vacancies and conductivity both increase after annealing in N 2 , contributing to more efficient hole carrier collection through defect state‐assisted band‐to‐band transition. However, dipoles formed at the c‐Si/MoO x interface during N 2 annealing, proposed according to the calculation of differential charge density, might hinder the hole transportation from c‐Si to MoO x . A 1‐nm Al 2 O 3 layer inserted between a‐Si:H(i) and MoO x is found to be effective for mitigating the V oc drop after annealing in air. The approaches exhibit great potential for implementing screen‐printing on MoO x SCH solar cells, rendering industrial production of MoO x SCH solar cells feasible.

This paper proposes a trajectory tracking method based on swarm intelligence perception algorithm to address the problems of insufficient trajectory tracking accuracy and limited anti-interference ability of grasping robotic arms in dynamic environments. This method constructs an integrated framework of “perception planning control”, which integrates the target pose data of visual sensors and the contact force information of force sensors through swarm intelligence algorithms to achieve real-time estimation of environmental dynamic information; Generate a smooth trajectory that satisfies the constraint conditions based on the estimation results, and design an adaptive controller to achieve precise trajectory tracking. The experimental results show that the tracking error of this method converges to within 0.3 mm in static scenes, the response time in dynamic scenes reaches 45 ms, and the error amplification in interference scenes is controlled at 12%. The comprehensive performance is significantly better than traditional PID control and robust control. The deep integration of swarm intelligence and multi-source perception has solved the problem of disconnection between perception and control in traditional methods, providing technical support for complex grasping tasks.

This study aims to develop a forecasting model that predicts the annual number of museum visitors by integrating structured museum-related data and unstructured sentiment data. While prior research has often relied on a single data type or traditional regression techniques, this study incorporates sentiment scores extracted from museum-related news articles and user comments to empirically assess the influence of external public opinion. Seven predictive algorithms including traditional models (Linear Regression and Random Forest Regressor) and deep learning models (RNN, GAN, CNN, LSTM, and Transformer) were evaluated for performance. Among these, the Transformer model demonstrated the highest predictive accuracy across all evaluation metrics (RMSE, MSLE, and MAPE) and was adopted as the final forecasting model. The results show that incorporating sentiment data significantly enhances forecasting precision, highlighting the substantial impact of media narratives and public sentiment on visitor behavior. This study offers a robust forecasting framework that integrates both structured and unstructured data, providing practical implications for sustainable museum planning and strategic decision-making.