A Shortest Distance Priority UAV Path Planning Algorithm for Precision Agriculture.

Unmanned aerial vehicles (UAVs) have made significant advances in autonomous sensing, particularly in the field of precision agriculture. Effective path planning is critical for autonomous navigation in large orchards to ensure that UAVs are able to recognize the optimal route between the start and end points. When UAVs perform tasks such as crop protection, monitoring, and data collection in orchard environments, they must be able to adapt to dynamic conditions.

Complex patterns and determinants of regional multiple chronic conditions across the United States.

Noncommunicable chronic diseases (NCDs) are a rapidly growing global public health concern, posing substantial challenges to healthcare systems. The presence of multiple (≥2) chronic conditions (MCC) exacerbates these challenges. In this study, we constructed an integrated MCC network to comprehensively evaluate the impact of NCD prevalence and associated factors on MCC patterns.

Multidimensional Directionality-Enhanced Segmentation via large vision model.

Optical Coherence Tomography (OCT) facilitates a comprehensive examination of macular edema and associated lesions. Manual delineation of retinal fluid is labor-intensive and error-prone, necessitating an automated diagnostic and therapeutic planning mechanism. Conventional supervised learning models are hindered by dataset limitations, while Transformer-based large vision models exhibit challenges in medical image segmentation, particularly in detecting small, subtle lesions in OCT images.

A Physically Constrained Deep-Learning Fusion Method for Estimating Surface NO Concentration from Satellite and Ground Monitors.

Accurate estimation of atmospheric chemical concentrations from multiple observations is crucial for assessing the health effects of air pollution. However, existing methods are limited by imbalanced samples from observations. Here, we introduce a novel deep-learning model-measurement fusion method (DeepMMF) constrained by physical laws inferred from a chemical transport model (CTM) to estimate NO concentrations over the Continental United States (CONUS).

Reusable SERS Platform of Femtosecond Laser Processed Substrate for Detection of Malachite Green.

This study presents the development of highly efficient Surface-Enhanced Raman Scattering (SERS) substrates through femtosecond (fs) laser processing of crystalline silicon (Si), resulting in mountain-like microstructures. These microstructures, when decorated with gold nanoparticles (Au NPs), exhibit remarkable SERS performance due to the creation of concentrated hotspots. The enhanced Raman signals originate from the excitation of localized surface plasmon resonance (LSPR) of the Au NPs and the multi-scale rough morphology of the Si substrates.

Diurnal hourly near-surface ozone concentration derived from geostationary satellite in China.

Near-surface O is a harmful atmospheric pollutant and a key component of urban photochemical pollution. The availability of satellite ozone concentration products is predominantly restricted to daytime, resulting in a lack of understanding of nighttime ozone pollution (e.g.

Rural Road Extraction in Xiong'an New Area of China Based on the RC-MSFNet Network Model.

High-resolution remote sensing imagery, reaching meter or sub-meter levels, provides essential data for extracting and identifying road information. However, rural roads are often narrow, elongated, and have blurred boundaries, with textures that resemble surrounding environments such as construction sites, vegetation, and farmland. These features often lead to incomplete extraction and low extraction accuracy of rural roads.

Spatiotemporal risk of human brucellosis under intensification of livestock keeping based on machine learning techniques in Shaanxi, China.

As one of the most neglected zoonotic diseases, brucellosis has posed a serious threat to public health worldwide. This study is purposed to apply different machine learning models to improve the prediction accuracy of human brucellosis (HB) in Shaanxi, China from 2008 to 2020, under livestock husbandry intensification from a spatiotemporal perspective. We quantitatively evaluated the performance and suitability of ConvLSTM, RF, and LSTM models in epidemic forecasting, and investigated the spatial heterogeneity of how different factors drive the occurrence and transmission of HB in distinct sub-regions by using Kernel Density Analysis and Shapley Additional Explanations.

Source-oriented stochastic health risk assessment of toxic metals in soil via a hybrid model and Monte Carlo simulation.

Toxic metal contamination in soils poses significant hazards to the environment and human health; thus, quantitative assessment of the sources and risks of metal contaminants are urgently needed. A hybrid model that integrates the positive matrix factorization (PMF) and random forest (RF) methods was proposed to quantify the sources of toxic metals in soils by combining diverse environmental variables (source proxies) in this study. In addition, a health risk assessment and Monte Carlo simulations were integrated to estimate the source-oriented stochastic health risk.

Cyanobacterial blooms prediction in China's large hypereutrophic lakes based on MODIS observations and Bayesian theory.

Cyanobacterial harmful algal blooms (HABs) pose a significant threat to aquatic ecosystems, water quality, and public health, particularly in large hypereutrophic lakes. Developing accurate short-term prediction models is essential for early warning and effective management of HABs. This study introduces a Bayesian-based model aimed at predicting HABs in three of China's large hypereutrophic lakes: Lake Taihu, Lake Chaohu, and Lake Hulunhu.

Association between community walkability and hypertension: Evidence from the Wuhan Chronic Disease Cohort Study.

While community walkability is recognized as a key environmental factor for health status, evidence linking it specifically to hypertension is rather limited. To fill the knowledge gap, we concluded a cross-sectional study among 6421 eligible participants from the Wuhan Chronic Disease Cohort. A well-developed algorithm was performed to evaluate community walkability across Wuhan, quantified as Walk Score.

A 30-m gross primary production dataset from 2016 to 2020 in China.

Estimating gross primary production (GPP) of terrestrial ecosystems is important for understanding the terrestrial carbon cycle. However, existed nationwide GPP datasets are primarily driven by coarse spatial resolutions (≥500 m) remotely sensed data, which fails to capture the spatial heterogeneity of GPP across different ecosystem types at land surface. This paper introduces a new GPP dataset, Hi-GLASS GPP v1, with a fine spatial resolution (30-m) and monthly temporal resolution from 2016 to 2020 in China.

Association of ambient temperature and diurnal temperature range with the outcome of in vitro fertilization in women from Hubei, China: A retrospective cohort study.

Objectives: To examine the relationship between ambient temperature and DTR and pregnancy outcomes in vitro fertilization/intracytoplasmic monosperm injection and embryo transfer (IVF/ICSI-ET) women.

Methods: The study included 5264 women who were treated with IVF/ICSI-ET at two centers in Hubei province from 2017 to 2022. The daily mean, daily maximum, and daily minimum temperatures at the subjects' home addresses were extracted, and DTR values were calculated based on latter two.

An estimation of future county-level cement production and associated air pollutant emissions in China through artificial neural networks.

Cement production and its air pollutant and carbon dioxides (CO) emissions in China will be relocated greatly as a joint effect of diverse development of industrial economy and implementation of environmental policies for different regions. The future pathway and spatial pattern of emissions are important for policy making of air quality improvement and CO emission abatement, as well as coordinating regional development. In this study, we developed an artificial neural network (ANN) model to predict cement production at the county level and to calculate the associated emissions of air pollutants and CO at the county level till 2060.

Air Quality, Health, and Equity Benefits of Carbon Neutrality and Clean Air Pathways in China.

In the pursuit of carbon neutrality, China's 2060 targets have been largely anchored in reducing greenhouse gas emissions, with less emphasis on the consequential benefits for air quality and public health. This study pivots to this critical nexus, exploring how China's carbon neutrality aligns with the World Health Organization's air quality guidelines (WHO AQG) regarding fine particulate matter (PM) exposure. Coupling a technology-rich integrated assessment model, an emission-concentration response surface model, and exposure and health assessment, we find that decarbonization reduces sulfur dioxide (SO), nitrogen oxides (NO), and PM emissions by more than 90%; reduces nonmethane volatile organic compounds (NMVOCs) by more than 50%; and simultaneously reduces the disparities across regions.

Short-term intermittent hypoxia exposure for dyspnea and fatigue in post-acute sequelae of COVID-19: A randomized controlled study.

Background: Post-acute sequelae of COVID-19 (PASC) is incurring a huge health and economic burden worldwide. There is currently no effective treatment or recommended drug for PASC.

Methods: This prospective randomized controlled study was conducted in a hospital in China.

Non-linear ground deformation detection and monitoring using time series InSAR along the coastal urban areas of Pakistan.

Conventional geodetic methods rely on point measurements, which have drawbacks for detecting and tracking geologic disasters at specific locations. In this study, the time series Interferometric Synthetic Aperture Radar (InSAR) approach was incorporated to estimate non-linear surface deformation caused by tectonic, shoreline reclamation, and other anthropogenic activities in economically important urban regions of Pakistan's southern coast, which possesses around 270 km. The shoreline is extended from the low-populated area on the premises of the Hub River in the west to the highly populated Karachi City and Eastern Industrial Zone, where we collected the Sentinel-1A C-band data from 2017 to 2023 to address urban security and threats to human life and property.

Graph Adaptive Attention Network with Cross-Entropy.

Non-Euclidean data, such as social networks and citation relationships between documents, have node and structural information. The Graph Convolutional Network (GCN) can automatically learn node features and association information between nodes. The core ideology of the Graph Convolutional Network is to aggregate node information by using edge information, thereby generating a new node feature.

(HTBNet)Arbitrary Shape Scene Text Detection with Binarization of Hyperbolic Tangent and Cross-Entropy.

The existing segmentation-based scene text detection methods mostly need complicated post-processing, and the post-processing operation is separated from the training process, which greatly reduces the detection performance. The previous method, DBNet, successfully simplified post-processing and integrated post-processing into a segmentation network. However, the training process of the model took a long time for 1200 epochs and the sensitivity to texts of various scales was lacking, leading to some text instances being missed.

National-scale 10-m maps of cropland use intensity in China during 2018-2023.

The amount of actively cultivated land in China is increasingly threatened by rapid urbanization and rural population aging. Quantifying the extent and changes of active cropland and cropping intensity is crucial to global food security. However, national-scale datasets for smallholder agriculture are limited in spatiotemporal continuity, resolution, and precision.

Nuclei-level prior knowledge constrained multiple instance learning for breast histopathology whole slide image classification.

New breast cancer cases have surpassed lung cancer, becoming the world's most prevalent cancer. Despite advancing medical image analysis, deep learning's lack of interpretability limits its adoption among pathologists. Hence, a nuclei-level prior knowledge constrained multiple instance learning (MIL) (NPKC-MIL) for breast whole slide image (WSI) classification is proposed.

Efficient Structure from Motion for Large-Size Videos from an Open Outdoor UAV Dataset.

Modern UAVs (unmanned aerial vehicles) equipped with video cameras can provide large-scale high-resolution video data. This poses significant challenges for structure from motion (SfM) and simultaneous localization and mapping (SLAM) algorithms, as most of them are developed for relatively small-scale and low-resolution scenes. In this paper, we present a video-based SfM method specifically designed for high-resolution large-size UAV videos.