Progress in UV Photodetectors Based on ZnO Nanomaterials: A Review of the Detection Mechanisms and Their Improvement.

Recent advancements in ultraviolet (UV) photodetection technology have driven intensive research on zinc oxide (ZnO) nanomaterials due to their exceptional optoelectronic properties. This review systematically examines the fundamental detection mechanisms in ZnO-based UV photodetectors (UVPDs), including photoconductivity effects, the threshold dimension phenomenon and light-modulated interface barriers. Based on these mechanisms, a large surface barrier due to surface-adsorbed O is generally constructed to achieve a high sensitivity.

Facile Synthesis of Polypyrrole/MnO/Carbon Cloth Composites for Supercapacitor Electrodes.

In the development of flexible smart electronics, fabricating electrodes with optimized architectures to achieve superior electrochemical performance remains a significant challenge. This study presents a two-step synthesis and characterization of a polypyrrole (PPy)-MnO/carbon cloth (CC) nanocomposite. The MnO/CC substrate was first prepared via the hydrothermal method, followed by uniform PPy coating through vapor-phase polymerization in the presence of an oxidizing agent.

Two-stage DRG grouping of cerebral infarction based on comorbidity and complications classification.

Background: Since 2017, cerebral infarction (CI) has become a leading cause of mortality in China, with rising treatment costs posing significant challenges to the healthcare system. The Diagnosis-Related Groups (DRG) payment system has been recognized as a potential solution to curb rising healthcare expenditures. However, in its implementation, China faces considerable hurdles due to its vast geographical size, regional economic disparities, and heterogeneous disease spectrum.

A new deep learning-based fast transcoding for internet of things applications.

To achieve low-power video communication in Internet of Things, this study presents a new deep learning-based fast transcoding algorithm from distributed video coding (DVC) to high efficiency video coding (HEVC). The proposed method accelerates transcoding by minimizing HEVC encoding complexity. Specifically, it models the selections of coding unit (CU) partitions and prediction unit (PU) partition modes as classification tasks.

An overview of computational methods in single-cell transcriptomic cell type annotation.

The rapid accumulation of single-cell RNA sequencing data has provided unprecedented computational resources for cell type annotation, significantly advancing our understanding of cellular heterogeneity. Leveraging gene expression profiles derived from transcriptomic data, researchers can accurately infer cell types, sparking the development of numerous innovative annotation methods. These methods utilize a range of strategies, including marker genes, correlation-based matching, and supervised learning, to classify cell types.

A segmentation method for oral CBCT image based on Segment Anything Model and semi-supervised teacher-student model.

Background: Accurate segmentation of oral cone beam computed tomography (CBCT) images is essential for research and clinical diagnosis. However, irregular and blurred tooth boundaries in CBCT images complicate the labeling of oral tissues, and insufficient labeled samples further limit the generalization ability of segmentation models. The Segment Anything Model (SAM) demonstrates strong generalization and segmentation accuracy across diverse tasks as a vision foundation model.

Normative structural connectome constrains spreading transient brain activity in generalized epilepsy.

Background: Genetic generalized epilepsy is characterized by transient episodes of spontaneous abnormal neural activity in anatomically distributed brain regions that ultimately propagate to wider areas. However, the connectome-based mechanisms shaping these abnormalities remain largely unknown. We aimed to investigate how the normative structural connectome constrains abnormal brain activity spread in genetic generalized epilepsy with generalized tonic-clonic seizure (GGE-GTCS).

Association of core brain networks with antipsychotic therapeutic effects in first-episode schizophrenia.

Elucidating neurobiological mechanisms underlying the heterogeneity of antipsychotic treatment will be of great value for precision medicine in schizophrenia, yet there has been limited progress. We combined static and dynamic functional connectivity (FC) analysis to examine the abnormal communications among core brain networks [default-mode network (DMN), central executive network (CEN), salience network (SN), primary network (PN), and subcortical network (SCN) in clinical subtypes of schizophrenia (responders and nonresponders to antipsychotic monotherapy). Resting-state functional magnetic resonance imaging data were collected from 79 first-episode schizophrenia and 90 healthy controls.

A Novel Framework for Remote Sensing Image Synthesis with Optimal Transport.

We propose a Generative Adversarial Network (GAN)-based method for image synthesis from remote sensing data. Remote sensing images (RSIs) are characterized by large intraclass variance and small interclass variance, which pose significant challenges for image synthesis. To address these issues, we design and incorporate two distinct attention modules into our GAN framework.

Integrated mRNA and miRNA Omics Analyses Reveal Transcriptional Regulation of the Tolerance Traits by in Response to High Temperature.

Within the context of global warming, understanding the molecular mechanisms behind physiological plasticity and local adaptation is essential for insect populations. This study performed an integrated miRNA and mRNA analysis on larvae exposed to temperatures of 20 °C, 24 °C, 28 °C, and 32 °C. Under varying thermal conditions, 1983 genes exhibited differential expression (i.

A new method for early diagnosis and treatment of meniscus injury of knee joint in student physical fitness tests based on deep learning method.

Introduction: Meniscus injuries in athletes' knee joints not only hinder performance but also pose substantial challenges in timely diagnosis and effective treatment. Delayed or inaccurate diagnosis often leads to prolonged recovery periods, exacerbating athletes' discomfort and compromising their ability to return to peak performance levels. Therefore, the accurate and timely diagnosis of meniscus injuries is crucial for athletes to receive appropriate treatment promptly and resume their training regimen effectively.

PCDe: A personalized conversational debiasing framework for next POI recommendation with uncertain check-ins.

In the next point-of-interest (POI) recommendation, users may visit individual POIs within larger gathering places, such as shopping malls (termed as collective POIs), leading to uncertain check-ins. Our data analysis unveils that (1) the presence of such uncertain check-ins raises a new type of bias, termed as scale bias, that is, the recommender tends to recommend collective POIs over individual POIs, which further exacerbates the commonly-observed popularity bias, that is, the recommender tends to recommend popular POIs rather than unpopular ones; and (2) the existence of the above two types of biases significantly affects the fairness of next POI recommendation with uncertain check-ins. Therefore, we propose a Personalized Conversational Debiasing framework (PCDe) by exploiting the advantages of conversational techniques to capture personalized dynamic user preferences, thereby mitigating both scale and popularity biases at a personalized level.

A Droplet-Based Electricity Nanogenerator with Press-Release Structure for Revealing the Coupling of Displacement and Conducting Currents.

Contact electrification (CE) and electrostatic induction (EI) are believed to be the core processes in classic liquid-solid triboelectric nanogenerators (L-S TENGs), including the classical transistor-like droplet-based electricity generator (DEG) and other forms of DEGs. Recently reported total current DEGs made full use of CE, EI, and charge transfer (CT) effects and realized the coupling of displacement and conducting currents. However, this method has only been revealed in special structures, which have limitations depending on the falling location of droplets.

Zr-doped porous NiP nanoarray as a highly efficient electrocatalyst for hydrogen evolution reaction in alkaline seawater.

The utilization of seawater electrolysis is recognized as a promising method for generating hydrogen as a substitute for conventional technology. Herein, the electrodes were fabricated bygrowth of Zr-NiP arrays on a nickel foam substrate (Zr-NiP/NF) through a low-temperature hydrothermal and phosphating process. The Zr-NiP/NF can achieve efficient hydrogen evolution reaction (HER) performance in alkaline seawater electrolyte.

A multi-domain constraint learning system inspired by adaptive cognitive graphs for emotion recognition.

Neuroscience shows that the brain stimulated by external information can induce functional responses to emotions, which can be measured and analyzed by electroencephalogram (EEG). Most existing works focus on extracting specific spatial topological information and temporal dependency representations, with a few works begin to mine the value of spatiotemporal cross-domain information. However, these approaches overdependence on cognitive prior information, limiting their ability to grasp complex domain-structured information.

Inversion of the global carbonaceous aerosol components (CACs) based on ground-based remote sensing of AERONET.

Carbonaceous aerosol components (CACs) significantly influence global radiative forcing and human health. We developed a simultaneous inversion algorithm for four CACs: black carbon (BC), brown carbon (BrC), water-soluble organic matter (WSOM), and water-insoluble organic matter (WIOM), considering their distinct optical, solubility, and hygroscopicity properties. Using AERONET data, we inverted the global concentrations of these components for 2022.

Advancements and Strategies in CsPbIBr Perovskite Solar Cells for Enhanced Efficiency and Stability.

In recent years, inorganic perovskite solar cells (IPSCs), especially those based on CsPbIBr, have attracted considerable attention owing to their exceptional thermal stability and a well-balanced combination of light absorption and phase stability. This review provides an extensive overview of the latest progress in CsPbIBr PSCs, focusing on film deposition techniques, crystallization control, interface engineering, and charge transport layers (CTLs). High-efficiency CsPbIBr PSCs can be achieved through the optimization of these key aspects.

An adaptive graph convolutional network with residual attention for emotion recognition.

Electroencephalogram (EEG)-based emotion recognition is a reliable and deployable method for identifying human emotional states. Currently, Graph convolution networks (GCN) have exhibited superior performance in extracting topological features of EEG. However, how to capture the dynamic topological relationship is still a challenge.

Early springtime O pollution episode in the Sichuan Basin: Transboundary and process analysis.

In the context of climate change, there is a trend of earlier onset and longer duration of ambient ozone (O) pollution in the Sichuan Basin (SCB). On 21-March 26, 2024, an O exceedance event occurred in the Chengdu Plain and southern SCB during early springtime, when moderate pollution rarely emerged before. The environmental and meteorological observation data, reanalysis dataset, and the WRF-Chem model coupling Integrated Process Rate (IPR) method were employed to examine the synoptic patterns and the transport mechanisms conducive to O pollution episode.

Normative modeling of brain morphometry in self-limited epilepsy with centrotemporal spikes.

Self-limited epilepsy with centrotemporal spikes is the most common pediatric epilepsy, characterized by an age-dependent onset that typically arises during childhood brain development and is followed by remission at puberty. However, the heterogeneity in children's brain development at the individual level complicates the challenge of personalized treatment. Our goal is to quantify individual deviations from the normative range of brain morphometric variation in children with Self-limited epilepsy with centrotemporal spikes and to assess their associations with clinical manifestations and cognitive functions.

Real-time mapping of gapless 24-hour surface PM in China.

Large-scale mapping of surface coarse particulate matter (PM) concentration remains a key focus for air quality monitoring. Satellite aerosol optical depth (AOD)-based data fusion approaches decouple the non-linear AOD-PM relationship, enabling high-resolution PM data acquisition, but are limited by spatial incompleteness and the absence of nighttime data. Here, a gridded visibility-based real-time surface PM retrieval (RT-SPMR) framework for China is introduced, addressing the gap in seamless hourly PM data within the 24-hour cycle.

BiFFN: Bi-Frequency Guided Feature Fusion Network for Visible-Infrared Person Re-Identification.

Visible-infrared person re-identification (VI-ReID) aims to minimize the modality gaps of pedestrian images across different modalities. Existing methods primarily focus on extracting cross-modality features from the spatial domain, which often limits the comprehensive extraction of useful information. Compared with conventional approaches that either focus on single-frequency components or employ simple multi-branch fusion strategies, our method fundamentally addresses the modality discrepancy through systematic frequency-space co-learning.

Psychological well-being and its associated factors among university students in Sichuan, China.

Introduction: Psychological well-being refers to a mental state that allows an individual to achieve their full potential, be productive and innovative in their work, and manage everyday stressors. This study aims to assess university students' psychological well-being by examining its associations with demographic factors such as gender, only child status, family background, grade level, financial aid status, and household income.

Methods: This cross-sectional, online study utilized the EPOCH Measure of Adolescent Well-being to examine five positive well-being indicators: Engagement, Perseverance, Optimism, Connectedness and Happiness among Chinese university students aged 18-25 years ( = 4,911).