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High-entropy alloys (HEAs) present both significant potential and challenges for developing efficient electrocatalysts due to their diverse combinations and compositions. Here, we propose a procedural approach that combines high-throughput experimentation with data-driven strategies to accelerate the discovery of efficient HEA electrocatalysts for the hydrogen evolution reaction (HER). This enables the rapid preparation of HEA arrays with various element combinations and composition ratios within a model system. The intrinsic activity of the HEA arrays is swiftly screened using scanning electrochemical cell microscopy (SECCM), providing precise composition-activity data sets for the HEA system. An ensemble machine learning (EML) model is then used to predict the activity database for the composition subspace of the system. Based on these database results, two groups of promising catalysts are recommended and validated through actual electrocatalytic evaluations. This procedural approach, which combines high-throughput experimentation with data-driven strategies, provides a new pathway to accelerate the discovery of efficient HEA electrocatalysts.

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Dual-atom catalysts (DACs) have garnered significant interest due to their remarkable catalytic reactivity. However, achieving atomically precise control in the fabrication of DACs remains a major challenge. Herein, we developed a straightforward and direct sublimation transformation synthesis strategy for dual-atom Fe catalysts (Fe2/NC) by utilizing in situ generated Fe2Cl6(g) dimers from FeCl3(s). The structure of Fe2/NC was investigated by aberration-corrected transmission electron microscopy and X-ray absorption fine structure (XAFS) spectroscopy. As-obtained Fe2/NC, with a Fe-Fe distance of 0.3 nm inherited from Fe2Cl6, displayed superior oxygen reduction performance with a half-wave potential of 0.90 V (vs. RHE), surpassing commercial Pt/C catalysts, Fe single-atom catalyst (Fe1/NC), and its counterpart with a common and shorter Fe-Fe distance of ~0.25 nm (Fe2/NC-S). Density functional theory (DFT) calculations and microkinetic analysis revealed the extended Fe-Fe distance in Fe2/NC is crucial for the O2 adsorption on catalytic sites and facilitating the subsequent protonation process, thereby boosting catalytic performance. This work not only introduces a new approach for fabricating atomically precise DACs, but also offers a deeper understanding of the intermetallic distance effect on dual-site catalysis.

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Renewable energy and advanced water treatment technologies hold profound significance for driving sustainable development in modern society. Given the environmental friendliness and high efficiency of electrocatalysis processes, great expectations are placed on their applications in energy and water-related fields. However, the electrocatalysis is limited by the selectivity, activity, and durability of the electrocatalytic reactions. Hydrogels, with their hierarchical porous structure, compositional and structural tunability, and ease of functionalization, are bringing surprising advances in advanced energy and environment. Hydrogel catalysts, inheriting the advantages of hydrogel materials, hold promise for achieving significant breakthroughs in electrochemical performance. Here, the latest advancements in energy and environmental electrocatalytic fields are summarized based on the 3D nanostructured hydrogel catalysts. In addition, future potentials and challenges of continuing research on hydrogel materials for energy and environment are discussed.

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In the traditional Deep Deterministic Policy Gradient (DDPG) algorithm, path planning for mobile robots in mapless environments still encounters challenges regarding learning efficiency and navigation performance, particularly adaptability and robustness to static and dynamic obstacles. To address these issues, in this study, an improved algorithm frame was proposed that designs the state and action spaces, and introduces a multi-step update strategy and a dual-noise mechanism to improve the reward function. These improvements significantly enhance the algorithm's learning efficiency and navigation performance, rendering it more adaptable and robust in complex mapless environments. Compared to the traditional DDPG algorithm, the improved algorithm shows a 20% increase in the stability of the navigation success rate with static obstacles along with a 25% reduction in pathfinding steps for smoother paths. In environments with dynamic obstacles, there is a remarkable 45% improvement in success rate. Real-world mobile robot tests further validated the feasibility and effectiveness of the algorithm in true mapless environments.

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Edible plant oils are a key component of the daily human diet, and the quality and safety of plant oils are related to human health. Perfluorinated and polyfluoroalkyl substances (PFASs) are pollutants that can contaminate plant oil through the processing of raw materials or exposure to materials containing these substances. Thus, establishing a sensitive and accurate analytical method for the determination of PFASs is critical for ensuring the safety of plant oils. In this study, a method based on acetonitrile extraction and solid phase extraction purification combined with ultra-high performance liquid chromatography-triple quadrupole mass spectrometry (UHPLC-MS/MS) was developed for the simultaneous determination of 21 PFASs, including perfluorocarboxylic acids, perfluoroalkyl sulfonic acids, and fluorotelomer sulfonic acids, in edible plant oils. The chromatographic conditions and MS parameters were optimized, and the influences of the extraction solvents and purification method were systematically studied. Plant oil samples were directly extracted with acetonitrile and purified using a weak anion-exchange (WAX) column. The 21 target PFASs were separated on a reversed-phase C18 chromatographic column and detected using a triple quadrupole mass spectrometer with an electrospray ionization source. The mass spectrometer was operated in negative-ion mode. The target compounds were analyzed in multiple reaction monitoring (MRM) mode and quantified using an internal standard method. The results demonstrated that the severe interference observed during the detection of PFASs in the co-extracted substances was completely eliminated after the extraction mixture was purified using a WAX column. The 21 target PFASs showed good linearity in their corresponding ranges, with correlation coefficients greater than 0.995. The limits of detection (LODs) and limits of quantification (LOQs) of the method were in the range of 0.004-0.015 and 0.015-0.050 µg/kg, respectively. The recoveries ranged from 95.6% to 115.8%, with relative standard deviations (RSDs) in the range of 0.3%-10.9% (n=9). The established method is characterized by simple sample pretreatment, good sensitivity, high immunity to interferences, and good stability, rendering it suitable for the rapid analysis and accurate determination of typical PFASs in edible plant oils.

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Achieving both high efficiency and high stability in blue thermally activated delayed fluorescence organic light-emitting diodes (TADF-OLEDs) is challenging for practical displays and lighting. Here, we have successfully developed a series of sky-blue to pure-blue emitting donor-acceptor (D-A) type TADF materials featuring a four-coordinated boron with 2,2'-(pyridine-2,6-diyl)diphenolate (dppy) ligands, i.e.1-8. Synergistic engineering of substituents on the phenyl bridge as well as the electronic properties and the attached positions of heteroatom N-donors not only enables fine-tuning of the emission colors, but also modulates the nature and energies of their triplet excited states that are important for the reverse intersystem crossing (RISC). Particularly for the compound with two methyl substituents on the phenyl bridge (compound 8), RISC is significantly facilitated through the vibronic coupling of the energetically close-lying triplet charge transfer (3CT) and the triplet local excited (3LE) states, when compared to analogue 7. Efficient sky-blue to pure-blue OLEDs with electroluminescence peaks (λ EL) at 460-492 nm have been obtained, in which ca. five-fold higher external quantum efficiencies (EQEs) of 18.9% have been demonstrated by 8 than that by 7. Moreover, ca. thirty times longer device operational half-lifetimes (LT50) of 9113 hours for 8 than that for 7 as well as satisfactory LT50 reaching 26 643 hours for 6 at an initial luminance of 100 cd m-2 have also been demonstrated. To the best of our knowledge, these results represent one of the best high-performance blue OLEDs based on tetracoordinated boron TADF emitters. Moreover, the design strategy presented here has provided an attractive strategy for enhancing the device performance of blue TADF-OLEDs.

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To gain a deeper understanding of the flowering pattern and reproductive characteristics of Epimedium sagittatum, to enrich the research on the flower development of E. sagittatum and its reproductive regulation, and to screen the methods suitable for the rapid detection of pollen viability of E. sagittatum and to promote its cross-breeding. The characteristics of its flower parts were observed, recorded and measured, and the pollen viability of E. sagittatumwas determined by five methods, including TTC staining, I2-KI staining, red ink staining, peroxidase method and in vitro germination method. The flowering process of E. sagittatum can be divided into five stages: calyx dehiscence, bract spathe, petal outgrowth, pollen dispersal, and pollination and withering. The results of I2-KI staining and peroxidase method were significantly higher than those of other methods; the in vitro germination method was intuitive and accurate, but the operation was complicated and time-consuming; the red ink staining method was easy to operate and had obvious staining effect, and the results were the closest to those of the in vitro germination method; and it was found that the pollen of E. sagittatum was not as effective as the in vitro germination method at the bud stamen stage, the flower stigma and the flower bud. It was also found that the pollen viability and germination rate of E. sagittatum pollen were higher in the three periods of bud spitting, petal adductor and pollen dispersal. Comparing the five methods, the red ink staining method was found to be a better method for the rapid detection of pollen viability; the best pollination periods of E. sagittatum were the bud stamen stage, petal adductor stage, and pollen dispersal stage of flowers at the peak of bloom. This study on the flowering and fruiting pattern of E. sagittatum, and the related mechanism of sexual reproduction, can be used as a reference for the next step of research on the breeding of E. sagittatum.

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Helicobacter pylori (H. pylori) infection is the primary risk factor for the progress of gastric diseases. The persistent stomach colonization of H. pylori is closely associated with the development of gastritis and malignancies. Although the involvement of progranulin (PGRN) in various cancer types has been well-documented, its functional role and underlying mechanisms in gastric cancer (GC) associated with H. pylori infection remain largely unknown. This report demonstrated that PGRN was up-regulated in GC and associated with poor prognosis, as determined through local and public database analysis. Additionally, H. pylori induced the up-regulation of PGRN in gastric epithelial cells both in vitro and in vivo. Functional studies have shown that PGRN promoted the intracellular colonization of H. pylori. Mechanistically, H. pylori infection induced autophagy, while PGRN inhibited autophagy to promote the intracellular colonization of H. pylori. Furthermore, PGRN suppressed H. pylori-induced autophagy by down-regulating decorin (DCN) through the mTOR pathway. In general, PGRN inhibited autophagy to facilitate intracellular colonization of H. pylori via the PGRN/mTOR/DCN axis. This study provides new insights into the molecular mechanisms underlying the progression of gastric diseases, suggesting PGRN as a potential therapeutic target and prognostic predictor for these disorders.

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The precise control of Z and E configurations of the carbon-carbon double bond in alkene synthesis has long been a fundamental challenge in synthetic chemistry, even more pronounced when simultaneously striving to achieve enantioselectivity [(Z,R), (Z,S), (E,R), (E,S)]. Moreover, enantiopure non-natural α-amino acids are highly sought after in organic and medicinal chemistry. In this study, we report a ligand-controlled stereodivergent synthesis of non-natural α-quaternary amino acids bearing trisubstituted alkene moieties in high yields with excellent enantioselectivity and Z/E selectivities. This success is achieved through a palladium/copper-cocatalyzed three-component assembly of readily available aryl iodides, allenes, and aldimine esters by simply tuning the chiral ligands of the palladium and copper catalysts.

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Non-ST-segment elevation acute myocardial infarction (NSTEMI) is a life-threatening clinical emergency with a poor prognosis. However, there are no individualized nomogram models to identify patients at high risk of NSTEMI who may undergo death. The aim of this study was to develop a nomogram for in-hospital mortality in patients with NSTEMI to facilitate rapid risk stratification of patients. A total of 774 non-diabetic patients with NSTEMI were included in this study. Least Absolute Shrinkage and Selection Operator regression was used to initially screen potential predictors. Univariate and multivariate logistic regression (backward stepwise selection) analyses were performed to identify the optimal predictors for the prediction model. The corresponding nomogram was constructed based on those predictors. The receiver operating characteristic curve, GiViTI calibration plot, and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. The nomogram model consisting of six predictors: age (OR = 1.10; 95% CI: 1.05-1.15), blood urea nitrogen (OR = 1.06; 95% CI: 1.00-1.12), albumin (OR = 0.93; 95% CI: 0.87-1.00), triglyceride (OR = 1.41; 95% CI: 1.09-2.00), D-dimer (OR = 1.39; 95% CI: 1.06-1.80), and aspirin (OR = 0.16; 95% CI: 0.06-0.42). The nomogram had good discrimination (area under the curve (AUC) = 0.89, 95% CI: 0.84-0.94), calibration, and clinical usefulness. In this study, we developed a nomogram model to predict in-hospital mortality in patients with NSTEMI based on common clinical indicators. The proposed nomogram has good performance, allowing rapid risk stratification of patients with NSTEMI.

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Atomically precise supported nanocluster catalysts (APSNCs), which feature exact atomic composition, well-defined structures, and unique catalytic properties, offer an exceptional platform for understanding the structure-performance relationship at the atomic level. However, fabricating APSNCs with precisely controlled and uniform metal atom numbers, as well as maintaining a stable structure, remains a significant challenge due to uncontrollable dispersion and easy aggregation during synthetic and catalytic processes. Herein, we developed an effective ligand engineering strategy to construct a Pt6 nanocluster catalyst stabilized on oxidized carbon nanotubes (Pt6/OCNT). The structural analysis revealed that Pt6 nanoclusters in Pt6/OCNT were fully exposed and exhibited a planar structure. Furthermore, the obtained Pt6/OCNT exhibited outstanding acidic HOR performances with a high mass activity of 18.37 A ⋅ mgpt -1 along with excellent stability during a 24 h constant operation and good CO tolerance, surpassing those of the commercial Pt/C. Density functional theory (DFT) calculations demonstrated that the unique geometric and electronic structures of Pt6 nanoclusters on OCNT altered the hydrogen adsorption energies on catalytic sites and thus lowered the HOR theoretical overpotential. This work presents a new prospect for designing and synthesizing advanced APSNCs for efficient energy electrocatalysis.

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convolutional neural networks (CNNs) show great potential in medical image segmentation tasks, and can provide reliable basis for disease diagnosis and clinical research. However, CNNs exhibit general limitations on modeling explicit long-range relation, and existing cures, resorting to building deep encoders along with aggressive downsampling operations, leads to loss of localized details. Transformer has naturally excellent ability to model the global features and long-range correlations of the input information, which is strongly complementary to the inductive bias of CNNs. In this paper, a novel Bi-directional Multi-scale Cascaded Segmentation Network, BMCS-Net, is proposed to improve the performance of medical segmentation tasks by aggregating these features obtained from Transformers and CNNs branches. Specifically, a novel feature integration technique, termed as Two-stream Cascaded Feature Aggregation (TCFA) module, is designed to fuse features in two-stream branches, and solve the problem of gradual dilution of global information in the network. Besides, a Multi-Scale Expansion-Aware (MSEA) module based on the convolution of feature perception and expansion is introduced to capture context information, and further compensate for the loss of details. Extensive experiments demonstrated that BMCS-Net has an excellent performance on both skin and Polyp segmentation datasets.

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AIMS: To devise effective preventive measures, a profound understanding of the evolving patterns and trends in atrial fibrillation (AF) and atrial flutter (AFL) burdens is pivotal. Our study was designed to quantify the burden and delineate the risk factors associated with AF and AFL across 204 countries and territories spanning 1990-2021. METHODS AND RESULTS: Data pertaining to AF and AFL were sourced from the Global Burden of Disease Study 2021. The burden of AF/AFL was evaluated using metrics such as incidence, disability-adjusted life years (DALYs), deaths, and their corresponding age-standardized rates (ASRs), stratified by age, sex, socio-demographic index (SDI), and human development index (HDI). The estimated annual percentage change was employed to quantify changes in ASRs. Population attributable fractions were calculated to determine the proportional contributions of major risk factors to age-standardized AF/AFL deaths. This analysis encompassed the period from 1990 to 2021. Globally, in 2021, there were 4.48 million incident cases [95% uncertainty interval (UI): 3.61-5.70], 8.36 million DALYs (95% UI: 6.97-10.13) and 0.34 million deaths (95% UI: 0.29-0.37) attributed to AF/AFL. The AF/AFL burden in 2021, as well as its trends from 1990 to 2021, displayed substantial variations based on gender, SDI quintiles, and geographical regions. High systolic blood pressure emerged as the leading contributor to age-standardized AF/AFL incidence, prevalence, death, and DALY rate globally among all potential risk factors, followed closely by high body mass index. CONCLUSION: Our study underscores the enduring significance of AF/AFL as a prominent public health concern worldwide, marked by profound regional and national variations. Despite the substantial potential for prevention and management of AF/AFL, there is a pressing imperative to adopt more cost-effective strategies and interventions to target modifiable risk factors, particularly in areas where the burden of AF/AFL is high or escalating.

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Friction as a fundamental physical phenomenon dominates nature and human civilization, among which the achievement of molecular rolling lubrication is desired to bring another breakthrough, like the macroscale design of wheel. Herein, an edge self-curling nanodeformation phenomenon of graphite nanosheets (GNSs) at cryogenic temperature is found, which is then used to promote the formation of graphite nanorollers in friction process towards molecular rolling lubrication. The observation of parallel nanorollers at the friction interface give the experimental evidence for the occurrence of molecular rolling lubrication, and the graphite exhibits abnormal lubrication performance in vacuum with ultra-low friction and wear at macroscale. The molecular rolling lubrication mechanism is elucidated from the electronic interaction perspective. Experiments and theoretical simulations indicate that the driving force of the self-curling is the uneven atomic shrinkage induced stress, and then the shear force promotes the intact nanoroller formation, while the constraint of atomic vibration decreases the dissipation of driving stress and favors the nanoroller formation therein. It will open up a new pathway for controlling friction at microscale and nanostructural manipulation.

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Hydrogen energy with its advantages of high calorific value, renewable nature, and zero carbon emissions is considered an ideal candidate for clean energy in the future. The electrochemical decomposition of water, powered by renewable and clean energy sources, presents a sustainable and environmentally friendly approach to hydrogen production. However, the traditional electrochemical overall water-splitting reaction (OWSR) is limited by the anodic oxygen evolution reaction (OER) with sluggish kinetics. Although important advances have been made in efficient OER catalysts, the theoretical thermodynamic difficulty predetermines the inevitable large potential (1.23 V vs. RHE for the OER) and high energy consumption for the conventional water electrolysis to obtain H2. Besides, the generation of reactive oxygen species at high oxidation potentials can lead to equipment degradation and increase maintenance costs. Therefore, to address these challenges, thermodynamically favorable anodic oxidation reactions with lower oxidation potentials than the OER are used to couple with the cathodic hydrogen evolution reaction (HER) to construct new coupling hydrogen production systems. Meanwhile, a series of robust catalysts applied in these new coupled systems are exploited to improve the energy conversion efficiency of hydrogen production. Besides, the electrochemical neutralization energy (ENE) of the asymmetric electrolytes with a pH gradient can further promote the decrease in application voltage and energy consumption for hydrogen production. In this review, we aim to provide an overview of the advancements in electrochemical hydrogen production strategies with low energy consumption, including (1) the traditional electrochemical overall water splitting reaction (OWSR, HER-OER); (2) the small molecule sacrificial agent oxidation reaction (SAOR) and (3) the electrochemical oxidation synthesis reaction (EOSR) coupling with the HER (HER-SAOR, HER-EOSR), respectively; (4) regulating the pH gradient of the cathodic and anodic electrolytes. The operating principle, advantages, and the latest progress of these hydrogen production systems are analyzed in detail. In particular, the recent progress in the catalytic materials applied to these coupled systems and the corresponding catalytic mechanism are further discussed. Furthermore, we also provide a perspective on the potential challenges and future directions to foster advancements in electrocatalytic green sustainable hydrogen production.

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BACKGROUND: Missed or delayed child healthcare caused by the COVID-19 lockdown has threatened young children's health and has had an unpredictable influence on caregivers' child healthcare preferences. This study investigated caregivers' child healthcare preferences and the factors that influence them among families with young children (0-3 years) during the lockdown in Shanghai. METHODS: Participants in this cross-sectional study were enrolled through random encounter sampling. Questionnaires were distributed online from June 1 to November 10, 2022, in Shanghai. A total of 477 valid questionnaires were received. The demographics of caregivers and their families, children's characteristics, COVID-19-related information, and caregivers' healthcare preferences were analyzed. The statistical analyses included frequency and percentage, chi-square tests, and multinomial logistic regression. RESULTS: Caregivers preferred child healthcare professionals in the community health service system (CHS; 47.6%) followed by hospital pediatricians (40.0%) during lockdown. Caregivers with the following characteristics preferred CHS: those with an annual household income of CNY 200,000-300,000, those whose youngest children were aged 8-12 months, and those who experienced early childhood physical development issues. Caregivers preferred hospitals if they had experienced healthcare-seeking-related difficulties in accessing professional guidance from hospital pediatricians. CONCLUSIONS: During pandemic lockdowns, policymakers should allocate more resources to CHS to meet caregivers' childcare demands. Moreover, special attention should be given to the healthcare needs for CHS among families with specific demographics. TRIAL REGISTRATION: Approval was obtained from the Ethics Committee of Shanghai Jiao Tong University School of Medicine School of Public Health (SJUPN-202,109; June 1, 2022).

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To conveniently implement the online detection of grain moisture in combined harvesters and the address the influence of the no-load measurement baseline, thereby enhancing detection accuracy and measurement continuity, this study developed a differential grain moisture detection device. For its convenient installation and integration on combined harvesters, a single-pole plate measurement element with a 1.6 mm thick epoxy resin coated with a 2-ounce copper film was designed, and a grain moisture detection device was constructed based on the STM32F103 microprocessor (STMicroelectronics International NV, Geneva, Switzerland). To enhance the device's interference resistance, a differential amplification measurement circuit integrated with high-frequency excitation was designed using a reference capacitance. To improve the resolution of the measurement circuit, Malab simulations were conducted at different excitation frequencies, ultimately selecting 30 kHz as the system's excitation signal frequency. To validate the effectiveness of the measurement circuit, validity tests were performed on the constructed sensor, which showed that the sensor's measurement voltage could effectively distinguish the moisture levels in grains, with a determination coefficient (R²) reaching 0.9978. To address the errors in moisture measurement caused by changes in grain temperature, an interaction experiment of the effect of moisture content and temperature on the measurement voltage was conducted using an integrated temperature sensor, resulting in the construction of a moisture content calculation model. Both the indoor static detection and field testing of the moisture detection device were conducted, indicating that the maximum average error in static measurements was 0.3%, with a maximum relative error of 0.47%, and the average relative error in field tests was ≤0.4%.

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The potential risk of heavy metals (HMs) to public health is an issue of great concern. Early prediction is an effective means to reduce the accumulation of HMs. The current prediction methods rarely take internal correlations between environmental factors into consideration, which negatively affects the accuracy of the prediction model and the interpretability of intrinsic mechanisms. Graph representation learning (GraRL) can simultaneously learn the attribute relationships between environmental factors and graph structural information. Herein, we developed the GraRL-HM method to predict the HM concentrations in soil-rice systems. The method consists of two modules, which are PeTPG and GCN-HM. In PeTPG, a graphic structure was generated using graph representation and communitization technology to explore the correlations and transmission paths of different environmental factors. Subsequently, the GCN-HM model based on the graph convolutional neural network (GCN) was used to predict the HM concentrations. The GraRL-HM method was validated by 2295 sets of data covering 21 environmental factors. The results indicated that the PeTPG model simplified correlation paths between factor nodes from 396 to 184, reducing by 53.5 % graph scale by eliminating the invalid paths. The concise and efficient graph structure enhanced the learning efficiency and representation accuracy of downstream prediction models. The GCN-HM model was superior to the four benchmark models in predicting the HM concentration in the crop, improving R2 by 36.1 %. This study develops a novel approach to improve the prediction accuracy of pollutant accumulation and provides valuable insights into intelligent regulation and planting guidance for heavy metal pollution control.

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Objective: Exploring the Incidence, Epidemic Trends, and Spatial Distribution Characteristics of Sporadic Hepatitis E in Hainan Province from 2013 to 2022 through four major tertiary hospitals in the Province. Methods: We collected data on confirmed cases of hepatitis E in Hainan residents admitted to the four major tertiary hospitals in Haikou City from January 2013 to December 2022. We used SPSS software to analyze the correlation between incidence rate and economy, population density and geographical location, and origin software to draw a scatter chart and SAS 9.4 software to conduct a descriptive analysis of the time trend. The distribution was analyzed using ArcMap 10.8 software (spatial autocorrelation analysis, hotspot identification, concentration, and dispersion trend analysis). SAS software was used to build an autoregressive integrated moving average model (ARIMA) to predict the monthly number of cases in 2023 and 2024. Results: From 2013 to 2022, 1,922 patients with sporadic hepatitis E were treated in the four hospitals of Hainan Province. The highest proportion of patients (n = 555, 28.88%) were aged 50-59 years. The annual incidence of hepatitis E increased from 2013 to 2019, with a slight decrease in 2020 and 2021 and an increase in 2022. The highest number of cases was reported in Haikou, followed by Dongfang and Danzhou. We found that there was a correlation between the economy, population density, latitude, and the number of cases, with the correlation coefficient |r| value fluctuating between 0.403 and 0.421, indicating a linear correlation. At the same time, a scatter plot shows the correlation between population density and incidence from 2013 to 2022, with r2 values fluctuating between 0.5405 and 0.7116, indicating a linear correlation. Global Moran's I, calculated through spatial autocorrelation analysis, showed that each year from 2013 to 2022 all had a Moran's I value >0, indicating positive spatial autocorrelation (p < 0.01). Local Moran's I analysis revealed that from 2013 to 2022, local hotspots were mainly concentrated in the northern part of Hainan Province, with Haikou, Wenchang, Ding'an, and Chengmai being frequent hotspot regions, whereas Baoting, Qiongzhong, and Ledong were frequent cold-spot regions. Concentration and dispersion analysis indicated a clear directional pattern in the average density distribution, moving from northeast to southwest. Time-series forecast modeling showed that the forecast number of newly reported cases per month remained relatively stable in 2023 and 2024, fluctuating between 17 and 19. Conclusion: The overall incidence of hepatitis E in Hainan Province remains relatively stable. The incidence of hepatitis E in Hainan Province increased from 2013 to 2019, with a higher clustering of cases in the northeast region and a gradual spread toward the southwest over time. The ARIMA model predicted a relatively stable number of new cases each month in 2023 and 2024.

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To date, five species of reddish-brown Neotriplax have been described, but their highly similar body color and other phenotypic traits make accurate taxonomy challenging. To clarify species-level taxonomy and validate potential new species, the cytochrome oxidase subunit I (COI) was used for phylogenetic analysis and the geometric morphometrics of elytron, pronotum, and hind wing were employed to distinguish all reddish-brown Neotriplax species. Phylogenetic results using maximum likelihood and Bayesian analyses of COI sequences aligned well with the current taxonomy of the Neotriplax species group. Significant K2P divergences, with no overlap between intra- and interspecific genetic distances, were obtained in Neotriplax species. The automatic barcode gap discovery (ABGD), assemble species by automatic partitioning (ASAP), and generalized mixed Yule coalescent (GMYC) approaches concurred, dividing the similar species into eight molecular operational taxonomic units (MOTUs). Geometric morphometric analysis using pronotum, elytron, hind wing shape and wing vein patterns also validated the classification of all eight species. By integrating these analytical approaches with morphological evidence, we successfully delineated the reddish-brown species of Neotriplax into eight species with three new species: N. qinghaiensis sp. nov., N. maoershanensis sp. nov., and N. guangxiensis sp. nov. Furthermore, we documented the first record of N. lewisii in China. This study underscores the utility of an integrative taxonomy approach in species delimitation within Neotriplax and serves as a reference for the taxonomic revision of other morphologically challenging beetles through integrative taxonomy.