Comparing circular and flexibly-shaped scan statistics for disease clustering detection.

The accuracy of spatial clustering detection is crucial for public health policy development and identifying etiological clues. Circular and flexibly-shaped scan statistics are widely used for disease cluster detection, but differences in results arise mainly due to parameter sensitivity and variations in the scanning window shapes. This study aims to analyze the impact of parameter settings on the results of these methods and compare their performance in disease clustering detection.

CTHNet: A CNN-Transformer Hybrid Network for Landslide Identification in Loess Plateau Regions Using High-Resolution Remote Sensing Images.

The Loess Plateau in northwest China features fragmented terrain and is prone to landslides. However, the complex environment of the Loess Plateau, combined with the inherent limitations of convolutional neural networks (CNNs), often results in false positives and missed detection for deep learning models based on CNNs when identifying landslides from high-resolution remote sensing images. To deal with this challenge, our research introduced a CNN-transformer hybrid network.

Segmented constitutive relationship of weakly cemented sandstone and numerical implementation based on homogeneity.

A deep understanding of the mechanical properties of weakly cemented sandstones in coal-bearing strata is crucial for ensuring the safety of coal mining operations. This study addresses this problem by investigating the deformation characteristics of such rocks through triaxial compression tests, and a novel piecewise constitutive model was developed, integrating the Double-strain Hoek model (TPHM) and statistical damage theory. The outcomes highlight several key findings: (1) The experiments revealed a distinct compaction stage in weakly cemented sandstone, which becomes shorter with an increase in confining pressure, highlighting a significant mechanical property of these rocks.

A new BWO-based RGB vegetation index and ensemble learning strategy for the pests and diseases monitoring of CCB trees using unmanned aerial vehicle.

Introduction: The Cinnamomum Camphora var. Borneol (CCB) tree is a valuable timber species with significant medicinal importance, widely cultivated in mountainous areas but susceptible to pests and diseases, making manual surveillance costly.

Methods: This paper proposes a method for detecting CCB pests and diseases using Unmanned aerial vehicle (UAV) as an advanced data collection carrier, capable of gathering large-scale data.

Regionally differentiated responses of chlorophyll-a concentrations to reduced human activity during COVID-19 lockdown in the San Francisco Bay area.

The COVID-19 lockdown created a unique opportunity to study the impact of reduced human activities on water quality. This study aimed to explore how changes in human activities, specifically reduced traffic emissions, influenced water quality in the San Francisco Bay Area from 2019 to 2021. Using chlorophyll-a (Chl-a) concentration as an indicator of water quality and NO₂ concentration as a proxy for traffic emissions, we analyzed the effects of reduced emissions on water quality across different regions of the Bay.

MOMFNet: A Deep Learning Approach for InSAR Phase Filtering Based on Multi-Objective Multi-Kernel Feature Extraction.

Interferometric Synthetic Aperture Radar (InSAR) is a widely used remote sensing technology for Earth observation, enabling the detection and measurement of ground deformation through the generation of interferograms. However, phase noise remains a critical factor that degrades interferogram quality. To address this issue, this study proposes MOMFNet, a deep learning approach for InSAR phase filtering based on multi-objective multi-kernel feature extraction that leverages multi-objective multi-kernel feature extraction.

A Large-Scale Building Unsupervised Extraction Method Leveraging Airborne LiDAR Point Clouds and Remote Sensing Images Based on a Dual P-Snake Model.

Automatic large-scale building extraction from the LiDAR point clouds and remote sensing images is a growing focus in the fields of the sensor applications and remote sensing. However, this building extraction task remains highly challenging due to the complexity of building sizes, shapes, and surrounding environments. In addition, the discreteness, sparsity, and irregular distribution of point clouds, lighting, and shadows, as well as occlusions of the images, also seriously affect the accuracy of building extraction.

Scale effects of supplementary nature reserves on biodiversity conservation in China's southern hilly region.

The Southern Hilly Region (SHR) of China plays a critical role in maintaining national ecological security and supporting global biodiversity. However, intensified human activities, such as urbanization and excessive land exploitation, have led to significant ecological degradation, posing threats to both local and global biodiversity. The current nature reserve system in SHR no longer meets the demands for local biodiversity conservation.

[Reversal Process and Driving Force Analysis for Arable Land Ecological Quality in Ningbo Using Time-series Remote Sensing Technology].

Timely monitoring of the changes in the ecological quality of arable land and the driving forces is of great significance for maintaining the ecological balance and sustainable development of agriculture. This study used the advanced time-series remote sensing continuous change detection and classification （CCDC） algorithm to synthesize images with the acquisition date of each year, in order to overcome the impacts of cloudy weather and vegetation phenology. Based on this, the reversal process and mechanism for the ecological quality of arable land in Ningbo were precisely identified using the comprehensive ecological evaluation index （CEEI） and geo-detector methods.

Determinants of spatiotemporal changes of land use carbon emissions for counties in Shaanxi Province, China.

In China, urban sprawl and developed land expansion challenge the country's "carbon peak" and "carbon neutrality" goals. Counties as the basic governance units are crucial for effective carbon reduction policies. This study examines land use carbon emissions (LUCE) in Shaanxi Province at the county level, essential for China's low-carbon strategy.

Construction of ecological security pattern in Loess Plateau counties by integrating structural connectivity and functional enhancement: A case study of Ansai District, Yan'an City, Northwest China.

Ecological security pattern is an important spatial way to maintain ecological processes and ensure the stability of ecosystem functions. As the implementation of landscape planning and decision-making, it is critically needed to consider the consistency of differentiated methods and their spatial outputs in the construction of ecological security patterns and the matching and applicability of research objects. From the perspective of integration, we combined the regional topography and landscape characteristics, integrated the morphological spatial pattern analysis and the importance evaluation results of ecosystem services to identify the ecological source, and constructed the ecological security pattern of the Ansai District of Yan'an City, the main implementation area of the Grain-for-Green Project on the Loess Plateau.

GNN-DDAS: Drug discovery for identifying anti-schistosome small molecules based on graph neural network.

Schistosomiasis is a tropical disease that poses a significant risk to hundreds of millions of people, yet often goes unnoticed. While praziquantel, a widely used anti-schistosome drug, has a low cost and a high cure rate, it has several drawbacks. These include ineffectiveness against schistosome larvae, reduced efficacy in young children, and emerging drug resistance.

MvGraphDTA: multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs.

Background: Accurately identifying drug-target affinity (DTA) plays a pivotal role in drug screening, design, and repurposing in pharmaceutical industry. It not only reduces the time, labor, and economic costs associated with biological experiments but also expedites drug development process. However, achieving the desired level of computational accuracy for DTA identification methods remains a significant challenge.

RS-Dseg: semantic segmentation of high-resolution remote sensing images based on a diffusion model component with unsupervised pretraining.

Semantic segmentation plays a crucial role in interpreting remote sensing images, especially in high-resolution scenarios where finer object details, complex spatial information and texture structures exist. To address the challenge of better extracting semantic information and ad-dressing class imbalance in multiclass segmentation, we propose utilizing diffusion models for remote sensing image semantic segmentation, along with a lightweight classification module based on a spatial-channel attention mechanism. Our approach incorporates unsupervised pretrained components with a classification module to accelerate model convergence.

Predicting nickel concentration in soil using fractional-order derivative and visible-near-infrared spectroscopy indices.

Accurate monitoring and estimation of heavy metal concentrations is an important process in the prevention and treatment of soil pollution. However, the weak correlation between spectra and heavy metals in soil makes it difficult to use spectroscopy in predicting areas with a risk of heavy metal pollution. In this paper, a method for detection of Ni in soil in eastern China using the fractional-order derivative (FOD) and spectral indices was proposed.

Data Quality Assessment of Gravity Recovery and Climate Experiment Follow-On Accelerometer.

Accelerometers are mainly used to measure the non-conservative forces at the center of mass of gravity satellites and are the core payloads of gravity satellites. All kinds of disturbances in the satellite platform and the environment will affect the quality of the accelerometer data. This paper focuses on the quality assessment of accelerometer data from the GRACE-FO satellites.

Climate change impacts on magnitude and frequency of urban floods under scenario and model uncertainties.

Many studies have confirmed that climate change leads to frequent urban flooding, which can lead to significant socioeconomic repercussions. However, most existing studies have not evaluated the impacts of climate change on urban flood from both event-scale and annual-scale dimensions. In addition, there are only few studies that simultaneously consider scenario and model uncertainties of climate change, and combine flood risk assessment and uncertainty analysis results to provide practical suggestions for urban drainage system management.

A Prolonged Artificial Nighttime-light Dataset of China (1984-2020).

Nighttime light remote sensing has been an increasingly important proxy for human activities. Despite an urgent need for long-term products and pilot explorations in synthesizing them, the publicly available long-term products are limited. A Night-Time Light convolutional LSTM network is proposed and applied the network to produce a 1-km annual Prolonged Artificial Nighttime-light DAtaset of China (PANDA-China) from 1984 to 2020.

Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning.

Background: Accurately identifying drug-target interaction (DTI), affinity (DTA), and binding sites (DTS) is crucial for drug screening, repositioning, and design, as well as for understanding the functions of target. Although there are a few online platforms based on deep learning for drug-target interaction, affinity, and binding sites identification, there is currently no integrated online platforms for all three aspects.

Results: Our solution, the novel integrated online platform Drug-Online, has been developed to facilitate drug screening, target identification, and understanding the functions of target in a progressive manner of "interaction-affinity-binding sites".

A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning.

Accurate calculation of drug-target affinity (DTA) is crucial for various applications in the pharmaceutical industry, including drug screening, design, and repurposing. However, traditional machine learning methods for calculating DTA often lack accuracy, posing a significant challenge in accurately predicting DTA. Fortunately, deep learning has emerged as a promising approach in computational biology, leading to the development of various deep learning-based methods for DTA prediction.