A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.

Neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio:Markers predicting immune-checkpoint inhibitor efficacy and immune-related adverse events

We conducted a comprehensive review of existing prediction models pertaining to the efficacy of immune-checkpoint inhibitor(ICI)and the occurrence of immune-related adverse events(irAEs).The predictive potential of neutrophil-to-lymphocyte ratio(NLR)and platelet-to-lymphocyte ratio(PLR)in determining ICI effectiveness has been extensively investigated,while limited research has been conducted on predicting irAEs.Furthermore,the combined model incor-porating NLR and PLR,either with each other or in conjunction with additional markers such as carcinoembryonic antigen,exhibits superior predictive capabilities compared to individual markers alone.NLR and PLR are promising markers for clinical applications.Forthcoming models ought to incorporate established efficacious models and newly identified ones,thereby constituting a multifactor composite model.Furthermore,efforts should be made to explore effective clinical application approaches that enhance the predictive accuracy and efficiency.

Value of improved nursing measures and enhanced nursing management to reduce the occurrence of adverse events in pediatric infusion

BACKGROUND Intravenous infusion is a common method of drug administration in clinical practice.Errors in any aspect of the infusion process,from the verification of medical orders,preparation of the drug solution,to infusion by nursing staff,may cause adverse infusion events.AIM To analyzed the value of improving nursing measures and enhancing nursing management to reduce the occurrence of adverse events in pediatric infusion.METHODS The clinical data of 130 children who received an infusion in the pediatric department of our hospital from May 2020 to May 2021 were analyzed and divided into two groups according to the differences in nursing measures and nursing management:65 patients in the control group received conventional nursing and nursing management interventions,while 65 patients in the observation group received improved nursing measure interventions and enhanced nursing management.The occurrence of adverse events,compliance of children,satisfaction of children’s families,and complaints regarding the transfusion treatment were recorded in both groups.RESULTS The incidence of fluid extravasation and infusion set dislodgement in the observation group were 3.08%and 1.54%,respectively,which were significantly lower than 12.31%and 13.85%in the control group(P<0.05),while repeated punctures and medication addition errors in the observation group were 3.08%and 0.00%,respectively,which were lower than 9.23%and 3.08%in the control group,but there was no significant difference(P>0.05).The compliance rate of children in the observation group was 98.46%(64/65),which was significantly higher than 87.69%(57/65)in the control group,and the satisfaction rate of children’s families was 96.92%(63/65),which was significantly higher than 86.15%(56/65)in the control group(P<0.05).The observation group did not receive any complaints from the child’s family,whereas the control group received four complaints,two of which were due to the crying of the child caused by repeated punctures,one due to the poor attitude of the nurse,and one due to medication addition errors,with a cumulative complaint rate of 6.15%.The cumulative complaint rate of the observation group was significantly lower than that of the control group(P<0.05).CONCLUSION Improving nursing measures and enhancing nursing management can reduce the incidence of fluid extravasation and infusion set dislodgement in pediatric patients,improve children’s compliance and satisfaction of their families,and reduce family complaints.

Prediabetes: An overlooked risk factor for major adverse cardiac and cerebrovascular events in atrial fibrillation patients

BACKGROUND Prediabetes is a well-established risk factor for major adverse cardiac and cerebrovascular events(MACCE).However,the relationship between prediabetes and MACCE in atrial fibrillation(AF)patients has not been extensively studied.Therefore,this study aimed to establish a link between prediabetes and MACCE in AF patients.AIM To investigate a link between prediabetes and MACCE in AF patients.METHODS We used the National Inpatient Sample(2019)and relevant ICD-10 CM codes to identify hospitalizations with AF and categorized them into groups with and without prediabetes,excluding diabetics.The primary outcome was MACCE(all-cause inpatient mortality,cardiac arrest including ventricular fibrillation,and stroke)in AF-related hospitalizations.RESULTS Of the 2965875 AF-related hospitalizations for MACCE,47505(1.6%)were among patients with prediabetes.The prediabetes cohort was relatively younger(median 75 vs 78 years),and often consisted of males(56.3%vs 51.4%),blacks(9.8%vs 7.9%),Hispanics(7.3%vs 4.3%),and Asians(4.7%vs 1.6%)than the non-prediabetic cohort(P<0.001).The prediabetes group had significantly higher rates of hypertension,hyperlipidemia,smoking,obesity,drug abuse,prior myocardial infarction,peripheral vascular disease,and hyperthyroidism(all P<0.05).The prediabetes cohort was often discharged routinely(51.1%vs 41.1%),but more frequently required home health care(23.6%vs 21.0%)and had higher costs.After adjusting for baseline characteristics or comorbidities,the prediabetes cohort with AF admissions showed a higher rate and significantly higher odds of MACCE compared to the non-prediabetic cohort[18.6%vs 14.7%,odds ratio(OR)1.34,95%confidence interval 1.26-1.42,P<0.001].On subgroup analyses,males had a stronger association(aOR 1.43)compared to females(aOR 1.22),whereas on the race-wise comparison,Hispanics(aOR 1.43)and Asians(aOR 1.36)had a stronger association with MACCE with prediabetes vs whites(aOR 1.33)and blacks(aOR 1.21).CONCLUSION This population-based study found a significant association between prediabetes and MACCE in AF patients.Therefore,there is a need for further research to actively screen and manage prediabetes in AF to prevent MACCE.

Predicting major adverse cardiovascular events after orthotopic liver transplantation using a supervised machine learning model:A cohort study

BACKGROUND Liver transplant(LT)patients have become older and sicker.The rate of post-LT major adverse cardiovascular events(MACE)has increased,and this in turn raises 30-d post-LT mortality.Noninvasive cardiac stress testing loses accuracy when applied to pre-LT cirrhotic patients.AIM To assess the feasibility and accuracy of a machine learning model used to predict post-LT MACE in a regional cohort.METHODS This retrospective cohort study involved 575 LT patients from a Southern Brazilian academic center.We developed a predictive model for post-LT MACE(defined as a composite outcome of stroke,new-onset heart failure,severe arrhythmia,and myocardial infarction)using the extreme gradient boosting(XGBoost)machine learning model.We addressed missing data(below 20%)for relevant variables using the k-nearest neighbor imputation method,calculating the mean from the ten nearest neighbors for each case.The modeling dataset included 83 features,encompassing patient and laboratory data,cirrhosis complications,and pre-LT cardiac assessments.Model performance was assessed using the area under the receiver operating characteristic curve(AUROC).We also employed Shapley additive explanations(SHAP)to interpret feature impacts.The dataset was split into training(75%)and testing(25%)sets.Calibration was evaluated using the Brier score.We followed Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines for reporting.Scikit-learn and SHAP in Python 3 were used for all analyses.The supplementary material includes code for model development and a user-friendly online MACE prediction calculator.RESULTS Of the 537 included patients,23(4.46%)developed in-hospital MACE,with a mean age at transplantation of 52.9 years.The majority,66.1%,were male.The XGBoost model achieved an impressive AUROC of 0.89 during the training stage.This model exhibited accuracy,precision,recall,and F1-score values of 0.84,0.85,0.80,and 0.79,respectively.Calibration,as assessed by the Brier score,indicated excellent model calibration with a score of 0.07.Furthermore,SHAP values highlighted the significance of certain variables in predicting postoperative MACE,with negative noninvasive cardiac stress testing,use of nonselective beta-blockers,direct bilirubin levels,blood type O,and dynamic alterations on myocardial perfusion scintigraphy being the most influential factors at the cohort-wide level.These results highlight the predictive capability of our XGBoost model in assessing the risk of post-LT MACE,making it a valuable tool for clinical practice.CONCLUSION Our study successfully assessed the feasibility and accuracy of the XGBoost machine learning model in predicting post-LT MACE,using both cardiovascular and hepatic variables.The model demonstrated impressive performance,aligning with literature findings,and exhibited excellent calibration.Notably,our cautious approach to prevent overfitting and data leakage suggests the stability of results when applied to prospective data,reinforcing the model’s value as a reliable tool for predicting post-LT MACE in clinical practice.

Resilient Event-Triggered Model Predictive Control for Adaptive Cruise Control Under Sensor Attacks

Dear Editor,This letter addresses the resilient model predictive control(MPC)problems for adaptive cruise control(ACC)systems under sensor attacks.In the light of vulnerabilities of ACC systems to sensor attacks,an intrusion detection mechanism is proposed at the controller side to distinguish abnormal data.Then,the robust control gains are derived to design the terminal region constraint for MPC.

Future Event Prediction Based on Temporal Knowledge Graph Embedding

Accurate prediction of future events brings great benefits and reduces losses for society in many domains,such as civil unrest,pandemics,and crimes.Knowledge graph is a general language for describing and modeling complex systems.Different types of events continually occur,which are often related to historical and concurrent events.In this paper,we formalize the future event prediction as a temporal knowledge graph reasoning problem.Most existing studies either conduct reasoning on static knowledge graphs or assume knowledges graphs of all timestamps are available during the training process.As a result,they cannot effectively reason over temporal knowledge graphs and predict events happening in the future.To address this problem,some recent works learn to infer future events based on historical eventbased temporal knowledge graphs.However,these methods do not comprehensively consider the latent patterns and influences behind historical events and concurrent events simultaneously.This paper proposes a new graph representation learning model,namely Recurrent Event Graph ATtention Network(RE-GAT),based on a novel historical and concurrent events attention-aware mechanism by modeling the event knowledge graph sequence recurrently.More specifically,our RE-GAT uses an attention-based historical events embedding module to encode past events,and employs an attention-based concurrent events embedding module to model the associations of events at the same timestamp.A translation-based decoder module and a learning objective are developed to optimize the embeddings of entities and relations.We evaluate our proposed method on four benchmark datasets.Extensive experimental results demonstrate the superiority of our RE-GAT model comparing to various base-lines,which proves that our method can more accurately predict what events are going to happen.

News event prediction by trigger evolution graph and event segment

Event prediction aims to predict the most possible following event given a chain of closely related context events.Previous methods based on event pairs or the entire event chain may ignore much structural and semantic information.Current datasets for event prediction,naturally,can be used for supervised learning.Event chains are either from document-level procedural action flow,or from news sequences under the same column.This paper leverages graph structure knowledge of event triggers and event segment information for event prediction with general news corpus,and adopts the standard multiple choice narrative cloze task evaluation.The topic model is utilized to extract event chains from the news corpus to deal with training data bottleneck.Based on trigger-guided structural relations in the event chains,we construct trigger evolution graph,and trigger representations are learned through graph convolutional neural network and the novel neighbor selection strategy.Then there are features of two levels for each event,namely,text level semantic feature and trigger level structural feature.We design the attention mechanism to learn the features of event segments derived in term of event major subjects,and integrate relevance between event segments and the candidate event.The most possible next event is picked by the relevance.Experimental results on the real-world news corpus verify the effectiveness of the proposed model.

Anthropogenic Influence on Decadal Changes in Concurrent Hot and Dry Events over China around the Mid-1990s

The frequency and duration of observed concurrent hot and dry events(HDEs) over China during the growing season(April–September) exhibit significant decadal changes across the mid-1990s. These changes are characterized by increases in HDE frequency and duration over most of China, with relatively large increases over southeastern China(SEC), northern China(NC), and northeastern China(NEC). The frequency of HDEs averaged over China in the present day(PD,1994–2011) is double that in the early period(EP, 1964–81);the duration of HDEs increases by 60%. Climate experiments with the Met Office Unified Model(MetUM-GOML2) are used to estimate the contributions of anthropogenic forcing to HDE decadal changes over China. Anthropogenic forcing changes can explain 60%–70% of the observed decadal changes,suggesting an important anthropogenic influence on HDE changes over China across the mid-1990s. Single-forcing experiments indicate that the increase in greenhouse gas(GHG) concentrations dominates the simulated decadal changes,increasing the frequency and duration of HDEs throughout China. The change in anthropogenic aerosol(AA) emissions significantly decreases the frequency and duration of HDEs over SEC and NC, but the magnitude of the decrease is much smaller than the increase induced by GHGs. The changes in HDEs in response to anthropogenic forcing are mainly due to the response of climatological mean surface air temperatures. The contributions from changes in variability and changes in climatological mean soil moisture and evapotranspiration are relatively small. The physical processes associated with the response of HDEs to GHG and AA changes are also revealed.

The Influence of Arctic Sea Ice Concentration Perturbations on Subseasonal Predictions of North Atlantic Oscillation Events

The influence of Arctic sea ice concentration (SIC) on the subseasonal prediction of the North Atlantic Oscillation (NAO) event is investigated by utilizing the Community Atmospheric Model version 4. The optimal Arctic SIC perturbations which exert the greatest influence on the onset of an NAO event from a lead of three pentads (15 days) are obtained with a conditional nonlinear optimal perturbation approach. Numerical results show that there are two types of optimal Arctic SIC perturbations for each NAO event, with one weakening event (marked as type-1) and another strengthening event (marked as type-2). For positive NAO events, type-1 optimal SIC perturbations mainly show positive SIC anomalies in the Greenland, Barents, and Okhotsk Seas, while type-2 perturbations mainly feature negative SIC anomalies in these regions. For negative NAO events, the optimal SIC perturbations have almost opposite patterns to those in positive events, although there are some differences among these SIC perturbations due to different atmospheric initial conditions. Further diagnosis reveals that the optimal Arctic SIC perturbations first modify the surface turbulent heat flux and the temperature in the lower troposphere via diabatic processes. Afterward, the temperature in the low troposphere is mainly affected by dynamic advection. Finally, potential vorticity advection plays a crucial role in the 500-hPa geopotential height prediction in the northern North Atlantic sector during pentad 4, which influences NAO event prediction. These results highlight the importance of Arctic SIC on NAO event prediction and the spatial characteristics of the SIC perturbations may provide scientific support for target observations of SIC in improving NAO subseasonal predictions.

外源6-BA对小苍兰‘Red Lion’生长及花期调控的影响

本试验以小苍兰品种‘Red Lion’为材料,研究喷施不同浓度6-BA(25、50、100 mg/L和200 mg/L)对小苍兰生长、生理生化特性及花期的影响,以期为小苍兰花期调控和切花栽培提供技术支持。结果表明:喷施适当浓度的6-BA有利于延长小苍兰‘Red Lion’花期;6-BA浓度为200 mg/L时显著提高小苍兰的花朵直径、花葶长度、小花数、株高、叶片数,较对照花期延长11 d、花朵直径增加50.45%、小花数增加114.89%,同时叶片的丙二醛含量较CK降低34.65%,叶片抗氧化酶(POD、CAT)活性升高,分别比CK提高97.26%和393.24%。综之,喷施适当浓度的6-BA能够显著提高小苍兰‘Red Lion’花朵质量及数量,延长小苍兰花期,其中200 mg/L 6-BA处理效果最佳。

Application Research on Two-Layer Threat Prediction Model Based on Event Graph

Advanced Persistent Threat(APT)is now the most common network assault.However,the existing threat analysis models cannot simultaneously predict the macro-development trend and micro-propagation path of APT attacks.They cannot provide rapid and accurate early warning and decision responses to the present system state because they are inadequate at deducing the risk evolution rules of network threats.To address the above problems,firstly,this paper constructs the multi-source threat element analysis ontology(MTEAO)by integrating multi-source network security knowledge bases.Subsequently,based on MTEAO,we propose a two-layer threat prediction model(TL-TPM)that combines the knowledge graph and the event graph.The macro-layer of TL-TPM is based on the knowledge graph to derive the propagation path of threats among devices and to correlate threat elements for threat warning and decision-making;The micro-layer ingeniously maps the attack graph onto the event graph and derives the evolution path of attack techniques based on the event graph to improve the explainability of the evolution of threat events.The experiment’s results demonstrate that TL-TPM can completely depict the threat development trend,and the early warning results are more precise and scientific,offering knowledge and guidance for active defense.

Red/ET同源重组介导的基因克隆载体pBACS的构建、鉴定与应用

目的通过Red/ET同源重组技术构建pBACS基因克隆及测序质粒载体。方法以质粒pBAC2015、pET28a为模板设计四对引物,PCR扩增得到四段彼此间有50 bp同源区域的基因片段,分别命名为bac-sop、bac-ori、bac-kan和bac-T7。利用Red/ET同源重组技术,将四段基因片段电转至E.coli GB05-dir感受态中进行线线重组。经酶切、测序验证阳性克隆质粒。此外,对重组质粒pBACS进行稳定性鉴定;利用pBACS分别进行细菌16S rDNA和真菌ITS基因克隆和基因测序。结果酶切和测序的验证结果显示,质粒的大小、元件方向及序列正确;pBACS具有较强的稳定性,适用于作为基因工程的克隆载体;可高效用于细菌16S rDNA和真菌ITS序列的克隆,阳性率为100%。结论成功利用Red/ET技术构建克隆载体pBACS,质粒载体具有稳定性强、重组效率高的特点,在基因克隆和基因测序等方面具有良好的应用潜力。

5G RedCap技术在电力行业应用的研究

Red Cap(5G轻量化)技术通过精简设备能力和降低复杂度,达到节约成本、降低设备功耗和缩小尺寸等目标,同时保留了5G原生能力,实现了技术和成本的平衡。本文研究了Red Cap的技术特性和相关国家政策,并结合电力行业网络需求,分析Red Cap在电力行业不同场景的应用和部署方案,探讨了Red Cap技术的发展潜力和运营商的部署策略。

Grassland-type ecosystem stability in China differs under the influence of drought and wet events

Ecological stability is a core issue in ecological research and holds significant implications forhumanity. The increased frequency and intensity of drought and wet climate events resulting from climatechange pose a major threat to global ecological stability. Variations in stability among different ecosystemshave been confirmed, but it remains unclear whether there are differences in stability within the sameterrestrial vegetation ecosystem under the influence of climate events in different directions and intensities.China's grassland ecosystem includes most grassland types and is a good choice for studying this issue.This study used the Standardized Precipitation Evapotranspiration Index-12 (SPEI-12) to identify thedirections and intensities of different types of climate events, and based on Normalized DifferenceVegetation Index (NDVI), calculated the resistance and resilience of different grassland types for 30consecutive years from 1990 to 2019 (resistance and resilience are important indicators to measurestability). Based on a traditional regression model, standardized methods were integrated to analyze theimpacts of the intensity and duration of drought and wet events on vegetation stability. The resultsshowed that meadow steppe exhibited the highest stability, while alpine steppe and desert steppe had thelowest overall stability. The stability of typical steppe, alpine meadow, temperate meadow was at anintermediate level. Regarding the impact of the duration and intensity of climate events on vegetationecosystem stability for the same grassland type, the resilience of desert steppe during drought was mainlyaffected by the duration. In contrast, the impact of intensity was not significant. However, alpine steppewas mainly affected by intensity in wet environments, and duration had no significant impact. Ourconclusions can provide decision support for the future grassland ecosystem governance.

Using Optical Tweezers to Study the Friction of the Red Blood Cells

In the last two decades the study of red blood cell elasticity using optical tweezers has known a rise appearing in the scientific research with regard to the various works carried out. Despite the various work done, no study has been done so far to study the influence of friction on the red blood cell indentation response using optical tweezers. In this study, we have developed a new approach to determine the coefficient of friction as well as the frictional forces of the red blood cell. This approach therefore allowed us to simultaneously carry out the indentation and traction test, which allowed us to extract the interfacial properties of the microbead red blood cell couple, among other things, the friction coefficient. This property would be extremely important to investigate the survival and mechanical features of cells, which will be of great physiological and pathological significance. But taking into account the hypothesis of friction as defined by the isotropic Coulomb law. The experiment performed for this purpose is the Brinell Hardness Test (DB).

Analysis and Assessment on the Heavy Metals in a Severely Degraded Subtropical Red Soil Region

5 different forests of Pinus massoniana, Schima superba, Liquidambar formosana, P. massoniana × S. superba, P. massoniana × L. formosana as the research object were set up to study the Cr, Cu and Zn content of degraded red soil region in subtropics. The soil heavy metal pollution degree was evaluated by national environmental quality standard (II class). The results showed that three soil metals of P. massoniana × S. superba were the highest, and the soil metals enrichment ability was strong. The order of single factor pollution index of metal elements was Cu (1.38) > Cr (0.81) > Zn (0.42), and moderately pollution, pollution warning and no pollution, respectively. There was no significant correlation between three soil heavy metals and soil total carbon (TC), total nitrogen (TN) and total phosphorus (TP). These results suggested that the accumulation of heavy metal elements was not derived from the parent material of soil. There was a significant positive correlation between the three metal elements which indicated that the sources of the three elements were similar. The structural equation model showed that the direct and indirect effects among the influencing factors ultimately affected the activity of heavy metals by cascade effects.

Solidification/Stabilization of Chromium in Red Mud-based Geopolymer

Up to 1.5wt%of Cr(Ⅲ)salts(CrCl_(3),and Cr_(2)O_(3))and Cr(Ⅵ)salts(Na_(2)CrO_(4),and CaCr_(2)O_(7))were incorporated into red mud-based geopolymers,respectively.The solidification/stabilization,compressive strength,and durability of the Cr-containing geopolymers were investigated.The experimental results indicate that the red mud-based geopolymer could effectively solidify/stabilize different types of Cr salts with solidification/stabilization rates of above 99.61%.Geopolymers are environmentally safe when the dosage of CaCr_(2)O_(7)is≤1.0wt%,or the dosage of CrCl_(3),Cr_(2)O_(3),and Na_(2)CrO_(4)is≤1.5wt%,respectively.The effects of Cr salts on the compressive strength varies with the type and content of Cr salts.The freeze-thaw cycle is more destructive to geopolymer properties than sulfate attack or acid rain erosion.The solidification/stabilization of Cr is mainly attributed to the following reasons:a)The chemical binding of Cr is related to the formation of Cr-containing hydrates(eg,magnesiochromite((Mg,Fe)(Cr,Al)_(2)O_(4)))and doping into N-A-S-H gel and C-A-S-H gel framework;b)The physical effect is related to the encapsulation by the hydration products(e g,N-A-S-H gel and C-A-S-H gel).This study provides a reference for the treatment of hazardous Cr-containing wastes by solid waste-based geopolymers.

Evolution model and failure mechanisms of rainfall-induced cracked red clay slopes:insights from Xinshao County,China

Red clay landslides are widely distributed worldwide,resulting in severe loss of life and property.Although rainfall-induced red clay slopes have received extensive attention,the role of cracks in the evolutionary process of red clay slopes and their connection to failure mechanisms is still poorly understood.A comprehensive approach integrating field investigation,laboratory tests,and numerical simulations was conducted to study the 168 red clay landslides in Xinshao County,China.The results show that red clay is prone to forming cracks at high moisture content due to its low swelling and high shrinkage properties.The failure mode of red clay slopes can be summarized in three stages:crack generation,slope excavation,and slope failure.Furthermore,the retrospective analysis and numerical simulations of the typical landslide in Guanchong indicated that intense rainfall primarily impacts the shallow layer of soil within approximately 0.5 m on the intact slope.However,cracks change the pattern of rainfall infiltration in the slope.Rainwater infiltrates rapidly through the preferential channels induced by the cracks rather than uniformly and slowly from the slope surface.This results in a significant increase in both the depth of infiltration and the saturated zone area of the cracked slope,reaching 3.8 m and 36.2 m^(2),respectively.Consequently,the factor of safety of the slope decreases by 13.4%compared to the intact slope,ultimately triggering landslides.This study can provide valuable insights into understanding the failure mechanisms of red clay slopes in China and other regions with similar geological settings.

A green cross-linking method for the preparation of renewable threedimensional graphene sponges for efficient adsorption of Congo red dye

Graphene-based materials possess significant potential for the treatment of dye wastewater due to their exceptional adsorption properties toward stubborn pollutants.However,their utilization is hindered by high preparation costs,low yields,environmental pollution during synthesis,and challenges in regenerating the adsorbent.This study proposes a novel approach to address these limitations by developing nitrogen-doped three-dimensional(3D)polyvinyl alcohol(PVA)crosslinked graphene sponges(N-PGA)using a cross-linking method with ammonium carbonate.This method offers a relatively mild,environmentally friendly approach.Ammonium carbonate serves as both a reducing and modifying agent,facilitating the formation of the intrinsic structure of N-PGA and acting as a nitrogen source.Meanwhile,PVA is utilized as the cross-linking agent.The results demonstrate that N-PGA exhibits a favorable internal 3D hierarchical porous structure and possesses robust mechanical properties.The measured specific surface area(BET)of N-PGA was as high as406.538 m^(2)·g^(-1),which was favorable for its efficient adsorption of Congo red(CR)dye molecules.At an initial concentration of 50 mg·L^(-1),N-PGA achieved an impressive removal rate of 89.6%and an adsorption capacity of 112 mg·g^(-1)for CR dye.Furthermore,it retained 79%of its initial adsorption capacity after 10 cycles,demonstrating excellent regeneration performance.In summary,the synthesized N-PGA displays remarkable efficacy in the adsorption of CR dye in wastewater,opening up new possibilities for utilizing 3D porous graphene nanomaterials as efficient adsorbents in wastewater treatment.