The effect of smoking on breast cancer screening results in female population: a study in Wu wei, China.

Objective: Based on Anderson model theory, a pathway analysis model of breast cancer screening population in this region was constructed to explore the impact of smoking on breast cancer screening results, in order to provide reference for refining breast cancer screening strategies and programs.

Methods: Firstly, the distribution of each variable in terms of whether breast related diseases were detected was described by single factor analysis. Then, based on the results of factor screening, a screening outcome pathway analysis model for female breast cancer screening population in 2021-2023 was constructed to determine the influence path and influence coefficient of smoking on breast cancer screening results.

HGCS-Det: A Deep Learning-Based Solution for Localizing and Recognizing Household Garbage in Complex Scenarios.

With the rise of deep learning technology, intelligent garbage detection provides a new idea for garbage classification management. However, due to the interference of complex environments, coupled with the influence of the irregular features of garbage, garbage detection in complex scenarios still faces significant challenges. Moreover, some of the existing research suffer from shortcomings in either their precision or real-time performance, particularly when applied to complex garbage detection scenarios.

Optimizing Backbone Networks Through Hybrid-Modal Fusion: A New Strategy for Waste Classification.

With rapid urbanization, effective waste classification is a critical challenge. Traditional manual methods are time-consuming, labor-intensive, costly, and error-prone, resulting in reduced accuracy. Deep learning has revolutionized this field.

YOLO-BCD: A Lightweight Multi-Module Fusion Network for Real-Time Sheep Pose Estimation.

The real-time monitoring of animal postures through computer vision techniques has become essential for modern precision livestock management. To overcome the limitations of current behavioral analysis systems in balancing computational efficiency and detection accuracy, this study develops an optimized deep learning framework named YOLOv8-BCD specifically designed for ovine posture recognition. The proposed architecture employs a multi-level lightweight design incorporating enhanced feature fusion mechanisms and spatial-channel attention modules, effectively improving detection performance in complex farm environments with occlusions and variable lighting.

Predicting the impact of dynamic global urban expansion on urban soil organic carbon.

Urban expansion has triggered significant changes in soil organic carbon (SOC), profoundly affecting the global carbon cycle. The accurate prediction of the global distribution of urban SOC and assessment of the impact of future urban expansion on SOC are essential for urban soil carbon management. By using data from 377 urban locations, this study estimated the global distribution of urban SOC and projected future SOC changes under two socioeconomic scenarios: SSP126 and SSP585.

Effect of an Endoscopy Screening on Upper Gastrointestinal Cancer Mortality: A Community-Based Multicenter Cluster Randomized Clinical Trial.

Background & Aims: Population-based observational studies suggest that endoscopic screening may reduce upper gastrointestinal cancer mortality. We aimed to quantify the effect of endoscopy screening.

Methods: This is a community-based, multicenter, cluster randomized clinical trial conducted in both high-risk and non-high-risk areas of China.

Global soil respiration estimation based on ecological big data and machine learning model.

Soil respiration (Rs) represents the greatest carbon dioxide flux from terrestrial ecosystems to the atmosphere. However, its environmental drivers are not fully understood, and there are still significant uncertainties in soil respiration model estimates. This study aimed to estimate the spatial distribution pattern and driving mechanism of global soil respiration by constructing a machine learning model method based on ecological big data.

Association of survival with adjuvant chemotherapy in patients with stage IB gastric cancer: a multicentre, observational, cohort study.

Background: Recurrence following radical resection in patients with stage IB gastric cancer (GC) is not uncommon. However, whether postoperative adjuvant chemotherapy could reduce the risk of recurrence in stage IB GC remains contentious.

Methods: We collected data on 2110 consecutive patients with pathologic stage IB (T1N1M0 or T2N0M0) GC who were admitted to 8 hospitals in China from 2009 to 2018.

An Improved EfficientNetV2 Model Based on Visual Attention Mechanism: Application to Identification of Cassava Disease.

With the characteristic of high recognition rate and strong network robustness, convolutional neural network has now become the most mainstream method in the field of crop disease recognition. Aiming at the problems with insufficient numbers of labeled samples, complex backgrounds of sample images, and difficult extraction of useful feature information, a novel algorithm is proposed in this study based on attention mechanisms and convolutional neural networks for cassava leaf recognition. Specifically, a combined data augmentation strategy for datasets is used to prevent single distribution of image datasets, and then the PDRNet (plant disease recognition network) combining channel attention mechanism and spatial attention mechanism is proposed.

A Method for Medical Microscopic Images' Sharpness Evaluation Based on NSST and Variance by Combining Time and Frequency Domains.

An algorithm for a sharpness evaluation of microscopic images based on non-subsampled shearlet wave transform (NSST) and variance is proposed in the present study for the purpose of improving the noise immunity and accuracy of a microscope's image autofocus. First, images are decomposed with the NSST algorithm; then, the decomposed sub-band images are subjected to variance to obtain the energy of the sub-band coefficients; and finally, the evaluation value is obtained from the ratio of the energy of the high- and low-frequency sub-band coefficients. The experimental results show that the proposed algorithm delivers better noise immunity performance than other methods reviewed by this study while maintaining high sensitivity.

Long-term trend in vegetation gross primary production, phenology and their relationships inferred from the FLUXNET data.

Climate-induced changes in plant phenology and physiology plays an important role in control of carbon exchange between terrestrial ecosystems and the atmosphere. Based on dataset during 1997-2014 from 41 flux tower sites (440 site-years) across the northern hemisphere, relationships between long-term trends in start of growing season (SOS), end of growing season (EOS), length of growing season (LOS), maximal gross primary production (GPP), and seasonal and annual gross primary production (GPP) were analyzed. Statistical Models of Integrated Phenology and Physiology (SMIPP) were built for predicting the long-term trends in annual GPP.

Improving Estimations of Spatial Distribution of Soil Respiration Using the Bayesian Maximum Entropy Algorithm and Soil Temperature as Auxiliary Data.

Soil respiration inherently shows strong spatial variability. It is difficult to obtain an accurate characterization of soil respiration with an insufficient number of monitoring points. However, it is expensive and cumbersome to deploy many sensors.