Assessing the monthly performance of daily precipitation products over Southeast Asia using the gauge-based analysis.

Accurate precipitation data is essential for understanding land surface processes and the hydrological cycle, particularly in Southeast Asia (SEA), where precipitation patterns are influenced by complex climatic interactions. This study evaluates the monthly performance of five widely-used daily precipitation products-CPC, CHIRPS, IMERG, ERA5, and PERSIANN-against the benchmark SAOBS dataset over SEA from January 2001 to December 2017. By aggregating daily data into monthly values, we identify the strengths and weaknesses of each product in capturing the spatial and temporal characteristics of precipitation in the tropical region.

An intelligent fault diagnosis model for bearings with adaptive hyperparameter tuning in multi-condition and limited sample scenarios.

Bearing fault diagnosis under multiple operating conditions is challenging due to the complexity of changing environments and the limited availability of training data. To address these issues, this paper presents an advanced diagnosis method using a hybrid Grey Wolf Algorithm (HGWA)-optimized convolutional neural network (CNN) and Bidirectional long short-term memory (BiLSTM) architecture. The proposed model leverages CNN for extracting spatial features and BiLSTM for capturing temporal dependencies.

Breaking the Activity-Stability Trade-Off of RuO via Metallic Ru Bilateral Regulation for Acidic Oxygen Evolution Reaction.

Developing highly efficient acidic oxygen evolution reaction (OER) electrocatalysts is crucial for proton exchange membrane water electrolyzer. RuO electrocatalysts, which followed a kinetically favorable lattice oxygen mechanism, perform a preferable intrinsic activity, but poor stability for acidic OER. Recent work often sacrifices the intrinsic activity of RuO to enhance stability.

Deep learning informed multimodal fusion of radiology and pathology to predict outcomes in HPV-associated oropharyngeal squamous cell carcinoma.

Background: We aim to predict outcomes of human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC), a subtype of head and neck cancer characterized with improved clinical outcome and better response to therapy. Pathology and radiology focused AI-based prognostic models have been independently developed for OPSCC, but their integration incorporating both primary tumour (PT) and metastatic cervical lymph node (LN) remains unexamined.

Methods: We investigate the prognostic value of an AI approach termed the swintransformer-based multimodal and multi-region data fusion framework (SMuRF).

Aqueous production of sulfur-containing aerosols from nitroaromatic compounds and SO in wintertime urban haze.

Nighttime aqueous oxidation of fossil fuel emissions is a significant source of atmospheric secondary organic aerosols. However, the underlying mechanism of the aqueous processing remains unclear. Utilizing ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry of water-soluble organic carbon samples, we present field observations that reveal the aqueous-phase conversion of nitroaromatic compounds (NACs) and sulfur-containing aerosols from fossil fuel combustion at high relative humidity during a severe haze event in Beijing in the winter of 2016.

[Characteristics and Influencing Factors of PM-O Composite Pollution in Henan Province].

The PM-O composite pollution in Henan Province has notable impacts on the economy, society, and daily life of the region, exhibiting a more complex nature than single pollution. Therefore, this study utilized air quality data, ground-based meteorological data, boundary layer height, CO concentration, emission inventory, and other information from 17 cities in Henan Province to investigate the spatial-temporal distribution, formation causes, and influencing factors of PM-O composite pollution from May 2014 to December 2023. The PSCF and CWT methods were used to identify possible source areas of composite pollution.

Multicolor Rare-Earth Film with Ultra-Long Afterglow for Diverse Energy-Saving Applications.

Rare-earth afterglow materials, with their unique light-storage properties, show great promise for diverse applications. However, their broader applicability is constrained by challenges such as poor solvent compatibility, limited luminescent efficiency, and monochromatic emissions. In this study, these limitations are addressed by blending ZnS with various rare-earth phosphors including (SrCa)S:Eu; SrAlO:Eu, Dy and SrMgSiO:Eu, Dy to modulate deep trap mechanisms and significantly enhance both the afterglow and light capture capabilities.

Assessment of atherosclerosis risk in an insufficient sample size based on K-Means BS and TW-gcForest.

Background: Insufficient data is a common issue encountered in studies of atherosclerosis risk assessment. However, when the sample size is insufficient, commonly used classification algorithms often fail to achieve superior performance, thereby limiting the application of atherosclerosis data in patient risk assessment.

Purpose: In cases where the sample size is inadequate, the use of an algorithmic model can allow for an effective evaluation of the risk of atherosclerosis in patients.

Amplified wintertime Arctic warming causes Eurasian cooling via nonlinear feedback of suppressed synoptic eddy activities.

The amplified wintertime Arctic warming has accelerated in recent decades. However, whether and how the warming can cause Eurasian cooling remains debated. By identifying daily Arctic warming events, we find direct observational evidence that the Arctic warming tends to cause substantial Eurasian cooling and an increase in occurrence frequency of Eurasian cooling events with a roughly 2-day lag.

Self-polymerization of carbonyl pigments for high-performance aqueous ammonium-ion batteries.

A self-polymerized carbonyl pigment, perylene-3,4,9,10-tetracarboxylic dianhydride (P420), is prepared by one-step heat treatment. Benefiting from molecular self-polymerization, the P420 electrode delivers a reversible capacity of 98.8 mA h g at 0.

GNSS-VTEC prediction based on CNN-GRU neural network model during high solar activities.

Total electron content (TEC), as a crucial ionospheric parameter, has impacts on electromagnetic wave propagation as well as satellite navigation and positioning, and is of great significance in space weather forecasting. Previous prediction efforts using neural network techniques have basically focused on years with relatively low solar activity. In this study, a model combining Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) network has been constructed to forecast the TEC during high solar activities from a single Global Navigation Satellite System (GNSS) receiver at Sanya in Hainan, China.

Video super resolution based on deformable 3D convolutional group fusion.

Video super resolution aims to generate high resolution video sequences from corresponding low resolution video sequences. Aiming at improving the insufficient utilization of temporal and spatial information of video sequences in current video super resolution methods, we proposed a new network based on deformable 3D convolutional group fusion. Input sequences were divided into groups according to different frame rates, which can effectively integrate time information in a hierarchical manner.

Potential of submerged macrophytes restoration for reducing CH and CO emissions in a typical urban lake.

As one of the areas most affected by human activities, urban lakes play a crucial role in global carbon cycle. Currently, submerged macrophyte restoration is a common ecological practice in urban lakes, primarily aimed at improving water quality and enhancing the aesthetic value of lake environments. Despite its widespread application, its contribution to carbon emission reduction has not received sufficient attention.

One-pot synthesized three-way junction based multiple strand displacement amplification for sensitive assay of H5N1 DNA.

The rapid and sensitive detection of H5N1, a highly pathogenic avian influenza virus, is crucial for controlling its spread and minimizing its impact on public health. In this study, we developed a novel biosensor based on strand displacement amplification (SDA) coupled with CRISPR/Cas12a for highly sensitive detection of H5N1 DNA. The biosensor utilizes a combination of a three-way junction structure, composed of three hairpins (H1, H2, H3), to initiate amplification through SDA, resulting in the production of numerous activators.

A Study on the Electrochemical Performance of SLMed Al6061/TiB2 Composite Anodes Caused by Laser Power.

Aluminum-air batteries have attracted more attention in recent years due to the theoretical possibility of replacing lithium batteries. Al6061/0.5wt.

Counterfactual explanations of tree based ensemble models for brain disease analysis with structure function coupling.

Convergent evidence has suggested that the disruption of either structural connectivity (SC) or functional connectivity (FC) in the brain can lead to various neuropsychiatric disorders. Since changes in SC-FC coupling may be more sensitive than a single modality to detect subtle brain connectivity abnormalities, a few learning-based methods have been proposed to explore the relationship between SC and FC. However, these existing methods still fail to explain the relationship between altered SC-FC coupling and brain disorders.

A comprehensive exploration of motion sickness process analysis from EEG signal and virtual reality.

Background And Objective: Virtual reality motion sickness is a significant barrier to the widespread adoption of virtual reality technology. Current virtual reality motion sickness detection methods using EEG signals often fail to identify comprehensive neuro-markers and lack generalizability across multiple subjects.

Methods: To address this issue, we analyzed the pre- and post-induction phases of virtual reality motion sickness, as well as the induction process, from multiple domain features.

Mixture Effects of Polystyrene Microplastics on the Gut Microbiota in C57BL/6 Mice.

Microplastics are plastic particles with sizes of less than 5 mm. The ubiquity of microplastics in the environment has raised serious public health concerns. Microplastics could disturb the composition of the gut microbiota due to both chemical composition and physical interactions, which might further influence the metabolism and immune function of the host.

Influences of synoptic circulations on regional transport, local accumulation and chemical transformation for PM heavy pollution over Twain-Hu Basin, central China.

The Twain-Hu Basin (THB), located in Central China, serves as a key juncture where the northerly "polluted" airflows of the East Asian winter monsoon meet the southerly warm and humid airflows. Using the T-PCA (T-mode Principal Component Analysis) objective synoptic pattern classification, Flexible Particle-Weather Research and Forecasting (FLEXPART-WRF) model, and Random Forest model, we investigate the influences of synoptic circulations on regional transport, local accumulation, and chemical transformation of PM during heavy air pollution over the THB in January of 2015-2022. The results show that the transport-type synoptic pattern accounts for 65.

Characteristics of airborne particles emitted from typical indoor combustion sources.

Combustion is an important source of indoor emissions, and exposure to combustion emissions not only concerns the quality of life of individuals but also directly affects the overall health level of society. To date, very few studies have examined the size-resolved emission characteristics of airborne particulate matter (PM) emitted from indoor sources. The study examined PM emissions from the specified combustion sources.

DuPt: Rehearsal-based continual learning with dual prompts.

The rehearsal-based continual learning methods usually involve reviewing a small number of representative samples to enable the network to learn new contents while retaining old knowledge. However, existing works overlook two crucial factors: (1) While the network prioritizes learning new data at incremental stages, it exhibits weaker generalization capabilities when trained individually on limited samples from specific categories, in contrast to training on large-scale samples across multiple categories simultaneously. (2) Knowledge distillation of a limited set of old samples can transfer certain existing knowledge, but imposing strong constraints may hinder knowledge transfer and restrict the ability of the network from the current stage to capture fresh knowledge.

Development of model for identifying homologous recombination deficiency (HRD) status of ovarian cancer with deep learning on whole slide images.

Background: Homologous recombination deficiency (HRD) refers to the dysfunction of homologous recombination repair (HRR) at the cellular level. The assessment of HRD status has the important significance for the formulation of treatment plans, efficacy evaluation, and prognosis prediction of patients with ovarian cancer.

Objectives: This study aimed to construct a deep learning-based classifier for identifying tumor regions from whole slide images (WSIs) and stratify the HRD status of patients with ovarian cancer (OC).

Fragmentation pattern-based nontargeted screening strategy uncovered novel halogenated nucleotides in drinking water.

Halogenated nucleotides have been reported as emerging disinfection byproducts in drinking water and the identification of halogenated nucleotides is a challenging but worthwhile endeavor due to their high risk, diverse chemical structures and scarcity of chemical standards. To improve the recognition efficiency of halogenated nucleotides in complex environmental matrices with multiple halogen ions, a fragmentation pattern-based nontargeted screening strategy was developed, which combines halogen isotope features and characteristic product ions to identify halogenated nucleotides within extensive datasets from high-resolution mass spectrometry. The data were preliminary screened based on the halogen isotope features of chemicals with varying halogen atom counts, followed by further identification using shared fragments with m/z values of 78.

Climate Change Drives Long-Term Spatiotemporal Shifts in Red Noctiluca scintillans Blooms Along China's Coast.

Climate change has significantly altered the spatiotemporal distribution and phenology of marine organisms, yet the long-term trends and mechanisms driving these changes remain insufficiently understood. In this study, we analysed historical Noctiluca scintillans bloom data from coastal China (1933, 1952, 1981-2023), sea surface temperature (SST) records from the past 40 years, and 509 field samples using Single Molecule Real-Time (SMRT) sequencing (2019-2024). Our results indicate that SST is the primary driver of N.

The impact of microplastic and sulfanilamide co-exposure on soil microbiota.

Microplastics, as emerging contaminants, can absorb antibiotics, and their coexistence in soil ecosystems poses serious threats to soil health. While previous studies have primarily focused on the individual effects of microplastics or antibiotics, the interactions between these pollutants in soil environments remain poorly understood. In this study, we investigated the combined effects of sulfonamide antibiotics and microplastics-both non-degradable low-density polyethylene and degradable polylactic acid-on soil microbiota and physicochemical properties.