Impact of stage-specific limb function exercises guided by a selfmanagement education model on arteriovenous fistula maturation status

BACKGROUND The exercise of limb function is the most economical and safe method to promote the maturation of arteriovenous fistula(AVF).However,due to the lack of a uni-fied exercise standard in China,many patients have insufficient awareness of the importance of AVF,leading to poor effectiveness of limb function exercise.The self-management education model can effectively promote patients to take pro-active health-related actions.This study focuses on the characteristics of patients during the peri-AVF period and conducts a phased limb function exercise under the guidance of the self-management education model to observe changes in fac-tors such as the maturity of AVF.AIM To assess the impact of stage-specific limb function exercises,directed by a self-management education model,on the maturation status of AVFs.METHODS This study is a randomized controlled trial involving 74 patients with forearm AVFs from the Nephrology Department of a tertiary hospital in Sichuan Province,China.Patients were randomly divided into an observation group and a control group using a random number table method.The observation group underwent tailored stage-specific limb func-tion exercises,informed by a self-management education model which took into account the unique features of AVF at various stages,in conjunction with routine care.Conversely,the control group was given standard limb function exercises along with routine care.The assessment involves the maturity of AVFs post-intervention,post-operative complications,and the self-management level of the fistula in both groups patients.Analyses were conducted using SPSS version 23.0.Count data were represented by frequency and percentage and subjected to chi-square test comparisons.Measurement data adhering to a normal distribution were presented as mean±SD.The independent samples t-test was utilized for inter-group comparisons,while the paired t-test was used for intra-group comparisons.For measurement data not fitting a normal distribution,the median and interquartile range were presented and analyzed using the Wilcoxon rank sum test.RESULTS At the 8-wk postoperative mark,the observation group demonstrated significantly higher scores in AVF symptom recognition,symptom prevention,and self-management compared to the control group(P<0.05).However,the variance in symptom management scores between the observation and control groups lacked statistical signi-ficance(P>0.05).At 4 wk after the operation,the observation group displayed a superior vessel diameter and depth from the skin of the drainage vessels in comparison to the control group(P<0.05).While the observation group did manifest elevated blood flow rates in the drainage vessels relative to the control group,this distinction was not statistically significant(P>0.05).By the 8-wk postoperative interval,the observation group outperformed the control group with notable enhancements in blood flow rates,vessel diameter,and depth from the skin of drainage vessels(P<0.01).Seven days following the procedure,the observation group manifested significantly diminished limb swelling and an overall reduced complication rate in contrast to the control group(P<0.05).The evaluation of infection,thrombosis,embolism,arterial aneurysm stenosis,and incision bleeding showed no notable differences between the two groups(P>0.05).By the 4-wk postoperative juncture,complications between the observation and control groups were statistically indistinguishable(P>0.05).CONCLUSION Stage-specific limb function exercises,under the guidance of a self-management education model,amplify the capacity of AVF patients to discern and prevent symptoms.Additionally,they expedite AVF maturation and miti-gate postoperative limb edema,underscoring their efficacy as a valuable method for the care and upkeep of AVF in hemodialysis patients.

耦合优化蚁群算法与P-Median model的选址模型设计

为节省城建部门对于公共体育设施的投入成本以及提高城市人民生活质量,以运动场所优化选址为例,提出一种新型设施选址模型。该模型主要基于P-Median model(最小化阻抗模型)根据需求点数量从全部候选设施选址中选择设施空间位置,让用户达到离自己最近设施距离成本总和最小的目的,对选址的基本原则和实际情况提出要求,构造目标函数用于优化后蚁群算法求解进行选址工作。优化蚁群算法实现基于Python语言模块,通过改进蚁群原始信息素,提升原有算法的收敛速度,求出目标函数最优解,可以很好地模拟对于运动场所的选址。用二者耦合进行优势互补所设计的选址模型来搜寻研究区蚁群信息素浓度残留最大的栅格像元,从而确定未被已有设施点服务半径覆盖的最佳设施点建立位置。实验结果表明,该新型选址模型相较于最小化阻抗模型与最大化覆盖模型,新增优化设施点使整体服务半径覆盖率分别高出10.42%和6.95%,适合求解较为精确且小规模空间下的选址问题。

Mshpy23:a user-friendly,parameterized model of magnetosheath conditions

Lunar Environment heliospheric X-ray Imager(LEXI)and Solar wind−Magnetosphere−Ionosphere Link Explorer(SMILE)will observe magnetosheath and its boundary motion in soft X-rays for understanding magnetopause reconnection modes under various solar wind conditions after their respective launches in 2024 and 2025.Magnetosheath conditions,namely,plasma density,velocity,and temperature,are key parameters for predicting and analyzing soft X-ray images from the LEXI and SMILE missions.We developed a userfriendly model of magnetosheath that parameterizes number density,velocity,temperature,and magnetic field by utilizing the global Magnetohydrodynamics(MHD)model as well as the pre-existing gas-dynamic and analytic models.Using this parameterized magnetosheath model,scientists can easily reconstruct expected soft X-ray images and utilize them for analysis of observed images of LEXI and SMILE without simulating the complicated global magnetosphere models.First,we created an MHD-based magnetosheath model by running a total of 14 OpenGGCM global MHD simulations under 7 solar wind densities(1,5,10,15,20,25,and 30 cm)and 2 interplanetary magnetic field Bz components(±4 nT),and then parameterizing the results in new magnetosheath conditions.We compared the magnetosheath model result with THEMIS statistical data and it showed good agreement with a weighted Pearson correlation coefficient greater than 0.77,especially for plasma density and plasma velocity.Second,we compiled a suite of magnetosheath models incorporating previous magnetosheath models(gas-dynamic,analytic),and did two case studies to test the performance.The MHD-based model was comparable to or better than the previous models while providing self-consistency among the magnetosheath parameters.Third,we constructed a tool to calculate a soft X-ray image from any given vantage point,which can support the planning and data analysis of the aforementioned LEXI and SMILE missions.A release of the code has been uploaded to a Github repository.

24Model与LCM原因因素定义对比研究

为探究损失致因模型(LCM)原因因素定义与事故致因“2-4”模型(24Model)存在的异同和优缺点,梳理2个模型各层面原因和结果的定义,对比定义内容及其对事故原因分析等安全实务的指导作用,并以一起瓦斯爆炸事故为例加以实证分析,获得二者分析结果之间的差异。研究结果表明:LCM是首个将管理因素纳入事故致因分析的一维事件序列模型,可明确各层面原因因素的定义和因素间的逻辑关系,但部分定义存在交叉重复的问题,并没有揭示安全工作指导思想等深层次事故致因因素;24Model作为系统性事故致因模型,对各类因素的定义均以组织为主体,描述事件、事故、安全的概念内涵,划分个体安全动作、安全能力和组织安全管理体系的类别并给出含义解析,探究组织安全文化层面的问题并以32个元素体现;2个模型的事故原因分析方法均建立在对各层级原因因素定义的基础上,并适用于模型理论体系本身。

Assessing the Performance of CMIP6 Models in Simulating Droughts across Global Drylands

Both the attribution of historical change and future projections of droughts rely heavily on climate modeling. However,reasonable drought simulations have remained a challenge, and the related performances of the current state-of-the-art Coupled Model Intercomparison Project phase 6(CMIP6) models remain unknown. Here, both the strengths and weaknesses of CMIP6 models in simulating droughts and corresponding hydrothermal conditions in drylands are assessed.While the general patterns of simulated meteorological elements in drylands resemble the observations, the annual precipitation is overestimated by ~33%(with a model spread of 2.3%–77.2%), along with an underestimation of potential evapotranspiration(PET) by ~32%(17.5%–47.2%). The water deficit condition, measured by the difference between precipitation and PET, is 50%(29.1%–71.7%) weaker than observations. The CMIP6 models show weaknesses in capturing the climate mean drought characteristics in drylands, particularly with the occurrence and duration largely underestimated in the hyperarid Afro-Asian areas. Nonetheless, the drought-associated meteorological anomalies, including reduced precipitation, warmer temperatures, higher evaporative demand, and increased water deficit conditions, are reasonably reproduced. The simulated magnitude of precipitation(water deficit) associated with dryland droughts is overestimated by 28%(24%) compared to observations. The observed increasing trends in drought fractional area,occurrence, and corresponding meteorological anomalies during 1980–2014 are reasonably reproduced. Still, the increase in drought characteristics, associated precipitation and water deficit are obviously underestimated after the late 1990s,especially for mild and moderate droughts, indicative of a weaker response of dryland drought changes to global warming in CMIP6 models. Our results suggest that it is imperative to employ bias correction approaches in drought-related studies over drylands by using CMIP6 outputs.

Genetically modified non-human primate models for research on neurodegenerative diseases

Neurodegenerative diseases(NDs)are a group of debilitating neurological disorders that primarily affect elderly populations and include Alzheimer's disease(AD),Parkinson's disease(PD),Huntington's disease(HD),and amyotrophic lateral sclerosis(ALS).Currently,there are no therapies available that can delay,stop,or reverse the pathological progression of NDs in clinical settings.As the population ages,NDs are imposing a huge burden on public health systems and affected families.Animal models are important tools for preclinical investigations to understand disease pathogenesis and test potential treatments.While numerous rodent models of NDs have been developed to enhance our understanding of disease mechanisms,the limited success of translating findings from animal models to clinical practice suggests that there is still a need to bridge this translation gap.Old World nonhuman primates(NHPs),such as rhesus,cynomolgus,and vervet monkeys,are phylogenetically,physiologically,biochemically,and behaviorally most relevant to humans.This is particularly evident in the similarity of the structure and function of their central nervous systems,rendering such species uniquely valuable for neuroscience research.Recently,the development of several genetically modified NHP models of NDs has successfully recapitulated key pathologies and revealed novel mechanisms.This review focuses on the efficacy of NHPs in modeling NDs and the novel pathological insights gained,as well as the challenges associated with the generation of such models and the complexities involved in their subsequent analysis.

Large animal models for Huntington's disease research

Huntington'sdisease(HD)isahereditary neurodegenerative disorder for which there is currently no effectivetreatmentavailable.Consequently,the development of appropriate disease models is critical to thoroughly investigate disease progression.The genetic basis of HD involves the abnormal expansion of CAG repeats in the huntingtin(HTT)gene,leading to the expansion of a polyglutamine repeat in the HTT protein.Mutant HTT carrying the expanded polyglutamine repeat undergoes misfolding and forms aggregates in the brain,which precipitate selective neuronal loss in specific brain regions.Animal models play an important role in elucidating the pathogenesis of neurodegenerative disorders such as HD and in identifying potential therapeutic targets.Due to the marked species differences between rodents and larger animals,substantial efforts have been directed toward establishing large animal models for HD research.These models are pivotal for advancing the discovery of novel therapeutic targets,enhancing effective drug delivery methods,and improving treatment outcomes.We have explored the advantages of utilizing large animal models,particularly pigs,in previous reviews.Since then,however,significant progress has been made in developing more sophisticated animal models that faithfully replicate the typical pathology of HD.In the current review,we provide a comprehensive overview of large animal models of HD,incorporating recent findings regarding the establishment of HD knock-in(KI)pigs and their genetic therapy.We also explore the utilization of large animal models in HD research,with a focus on sheep,non-human primates(NHPs),and pigs.Our objective is to provide valuable insights into the application of these large animal models for the investigation and treatment of neurodegenerative disorders.

Genetically modified pigs:Emerging animal models for hereditary hearing loss

Hereditary hearing loss(HHL),a genetic disorder that impairs auditory function,significantly affects quality of life and incurs substantial economic losses for society.To investigate the underlying causes of HHL and evaluate therapeutic outcomes,appropriate animal models are necessary.Pigs have been extensively used as valuable large animal models in biomedical research.In this review,we highlight the advantages of pig models in terms of ear anatomy,inner ear morphology,and electrophysiological characteristics,as well as recent advancements in the development of distinct genetically modified porcine models of hearing loss.Additionally,we discuss the prospects,challenges,and recommendations regarding the use pig models in HHL research.Overall,this review provides insights and perspectives for future studies on HHL using porcine models.

Genome-edited rabbits:Unleashing the potential of a promising experimental animal model across diverse diseases

Animal models are extensively used in all aspects of biomedical research,with substantial contributions to our understanding of diseases,the development of pharmaceuticals,and the exploration of gene functions.The field of genome modification in rabbits has progressed slowly.However,recent advancements,particularly in CRISPR/Cas9-related technologies,have catalyzed the successful development of various genome-edited rabbit models to mimic diverse diseases,including cardiovascular disorders,immunodeficiencies,agingrelated ailments,neurological diseases,and ophthalmic pathologies.These models hold great promise in advancing biomedical research due to their closer physiological and biochemical resemblance to humans compared to mice.This review aims to summarize the novel gene-editing approaches currently available for rabbits and present the applications and prospects of such models in biomedicine,underscoring their impact and future potential in translational medicine.

Relationship between clinical belonging,professional identity,and nursing information ability among nursing interns:Model construction

BACKGROUND Clinical belonging refers to the feeling that clinical medical staff feel recognized and accepted by others or groups.The level of clinical belonging of nursing interns affects students’learning motivation and confidence,which in turn affects their clinical practice behavior.AIM To explore the effects of professional identity and nursing information ability on clinical belonging among nursing interns and establish a relationship model for these factors.METHODS The researchers used the convenience sampling method to select 682 nursing interns from China.The survey was conducted using a general information questionnaire,clinical sense of belonging scale,nursing information ability self-assessment scale,and a nursing student professional identity questionnaire.The mediating effect of nursing information ability between their professional identity and clinical sense of belonging was analyzed using SPSS 21.0 and the path analysis in structural equation modeling.RESULTS The total scores of clinical belonging,professional identity,and nursing information ability of nursing interns were(104.29±13.11)points,(57.89±7.16)points,and(70.29±6.20)points,respectively.Nursing information ability had a direct effect on the clinical sense of belonging(effect value=0.46,P<0.05).Occupational identity had a direct effect(effect value=0.52,P<0.05)and an indirect effect(effect value=0.21,P<0.05)on clinical belonging.CONCLUSION Nursing administrators in nursing colleges and hospitals should take effective measures to improve the professional identity and nursing information ability of nursing interns,as well as the clinical sense of belonging among nursing interns.

Background removal from global auroral images:Data-driven dayglow modeling

Global images of auroras obtained by cameras on spacecraft are a key tool for studying the near-Earth environment.However,the cameras are sensitive not only to auroral emissions produced by precipitating particles,but also to dayglow emissions produced by photoelectrons induced by sunlight.Nightglow emissions and scattered sunlight can contribute to the background signal.To fully utilize such images in space science,background contamination must be removed to isolate the auroral signal.Here we outline a data-driven approach to modeling the background intensity in multiple images by formulating linear inverse problems based on B-splines and spherical harmonics.The approach is robust,flexible,and iteratively deselects outliers,such as auroral emissions.The final model is smooth across the terminator and accounts for slow temporal variations and large-scale asymmetries in the dayglow.We demonstrate the model by using the three far ultraviolet cameras on the Imager for Magnetopause-to-Aurora Global Exploration(IMAGE)mission.The method can be applied to historical missions and is relevant for upcoming missions,such as the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)mission.

Fractal Fractional Order Operators in Computational Techniques for Mathematical Models in Epidemiology

New fractional operators, the COVID-19 model has been studied in this paper. By using different numericaltechniques and the time fractional parameters, the mechanical characteristics of the fractional order model areidentified. The uniqueness and existence have been established. Themodel’sUlam-Hyers stability analysis has beenfound. In order to justify the theoretical results, numerical simulations are carried out for the presented methodin the range of fractional order to show the implications of fractional and fractal orders.We applied very effectivenumerical techniques to obtain the solutions of themodel and simulations. Also, we present conditions of existencefor a solution to the proposed epidemicmodel and to calculate the reproduction number in certain state conditionsof the analyzed dynamic system. COVID-19 fractional order model for the case of Wuhan, China, is offered foranalysis with simulations in order to determine the possible efficacy of Coronavirus disease transmission in theCommunity. For this reason, we employed the COVID-19 fractal fractional derivative model in the example ofWuhan, China, with the given beginning conditions. In conclusion, again the mathematical models with fractionaloperators can facilitate the improvement of decision-making for measures to be taken in the management of anepidemic situation.

Effect of health education based on information-motivationbehavioral skills model on patients with unilateral vestibular dysfunction

BACKGROUND Vestibular dysfunction(VH)is a common concomitant symptom of late peri-pheral vestibular lesions,which can be trauma,poisoning,infection,heredity,and neurodegeneration,but about 50%of the causes are unknown.The study uses the information-motivation-behavioral skills(IMB)model for health education,effectively improve the quality of life,increase their self-confidence,reduce anxiety and depression,and effectively improve the psychological state of patients.AIM To explore the effect of health education based on the IMB model on the degree of vertigo,disability,anxiety and depression in patients with unilateral vestibular hypofunction.METHODS The clinical data of 80 patients with unilateral vestibular hypofunction from January 2019 to December 2021 were selected as the retrospective research objects,and they were divided into the control group and the observation group with 40 cases in each group according to different nursing methods.Among them,the control group was given routine nursing health education and guidance,and the observation group was given health education and guidance based on the IMB model.The changes in self-efficacy,anxiety and depression,and quality of life of patients with unilateral VH were compared between the two groups.RESULTS There was no significant difference in General Self-Efficacy Scale(GSES)scale scores between the two groups of patients before nursing(P>0.05),which was comparable;after nursing,the GSES scale scores of the two groups were higher than those before nursing.The nursing group was higher than the control group,and the difference was statistically significant(P<0.05).There was no significant difference in the scores of Hospital Anxiety and Depression Scale(HADS)and anxiety and depression subscales between the two groups before nursing(P>0.05).After nursing,the HADS score,anxiety,and depression subscale scores of the two groups of patients were lower than those before nursing,and the nursing group was lower than the control group,and the difference was statistically significant(P<0.05).After nursing,the Dizziness Handicap Inventory(DHI)scale and DHI-P,DHI-E and DHI-F scores in the two groups were decreased,and the scores in the nursing group were lower than those in the control group,and the difference was statistically significant(P<0.05).CONCLUSION Health education based on the IMB model can effectively improve patients'quality of life,increase self-efficacy of patients with unilateral vestibular hypofunction,enhance patients'confidence,enable patients to resume normal work and life as soon as possible,reduce patients'anxiety and depression,and effectively improve patients'psychological status.

Model reduction of fractional impedance spectra for time–frequency analysis of batteries, fuel cells, and supercapacitors

Joint time–frequency analysis is an emerging method for interpreting the underlying physics in fuel cells,batteries,and supercapacitors.To increase the reliability of time–frequency analysis,a theoretical correlation between frequency-domain stationary analysis and time-domain transient analysis is urgently required.The present work formularizes a thorough model reduction of fractional impedance spectra for electrochemical energy devices involving not only the model reduction from fractional-order models to integer-order models and from high-to low-order RC circuits but also insight into the evolution of the characteristic time constants during the whole reduction process.The following work has been carried out:(i)the model-reduction theory is addressed for typical Warburg elements and RC circuits based on the continued fraction expansion theory and the response error minimization technique,respectively;(ii)the order effect on the model reduction of typical Warburg elements is quantitatively evaluated by time–frequency analysis;(iii)the results of time–frequency analysis are confirmed to be useful to determine the reduction order in terms of the kinetic information needed to be captured;and(iv)the results of time–frequency analysis are validated for the model reduction of fractional impedance spectra for lithium-ion batteries,supercapacitors,and solid oxide fuel cells.In turn,the numerical validation has demonstrated the powerful function of the joint time–frequency analysis.The thorough model reduction of fractional impedance spectra addressed in the present work not only clarifies the relationship between time-domain transient analysis and frequency-domain stationary analysis but also enhances the reliability of the joint time–frequency analysis for electrochemical energy devices.

Construction of a Computational Scheme for the Fuzzy HIV/AIDS Epidemic Model with a Nonlinear Saturated Incidence Rate

This work aimed to construct an epidemic model with fuzzy parameters.Since the classical epidemic model doesnot elaborate on the successful interaction of susceptible and infective people,the constructed fuzzy epidemicmodel discusses the more detailed versions of the interactions between infective and susceptible people.Thenext-generation matrix approach is employed to find the reproduction number of a deterministic model.Thesensitivity analysis and local stability analysis of the systemare also provided.For solving the fuzzy epidemic model,a numerical scheme is constructed which consists of three time levels.The numerical scheme has an advantage overthe existing forward Euler scheme for determining the conditions of getting the positive solution.The establishedscheme also has an advantage over existing non-standard finite difference methods in terms of order of accuracy.The stability of the scheme for the considered fuzzy model is also provided.From the plotted results,it can beobserved that susceptible people decay by rising interaction parameters.

Evaluation of the effects of health education interventions for hypertensive patients based on the health belief model

BACKGROUND Hypertension is a major risk factor for cardiovascular disease and stroke,and its prevalence is increasing worldwide.Health education interventions based on the health belief model(HBM)can improve the knowledge,attitudes,and behaviors of patients with hypertension and help them control their blood pressure.AIM To evaluate the effects of health education interventions based on the HBM in patients with hypertension in China.METHODS Between 2021 and 2023,140 patients with hypertension were randomly assigned to either the intervention or control group.The intervention group received health education based on the HBM,including lectures,brochures,videos,and counseling sessions,whereas the control group received routine care.Outcomes were measured at baseline,three months,and six months after the intervention and included blood pressure,medication adherence,self-efficacy,and perceived benefits,barriers,susceptibility,and severity.RESULTS The intervention group had significantly lower systolic blood pressure[mean difference(MD):-8.2 mmHg,P<0.001]and diastolic blood pressure(MD:-5.1 mmHg,P=0.002)compared to the control group at six months.The intervention group also had higher medication adherence(MD:1.8,P<0.001),self-efficacy(MD:12.4,P<0.001),perceived benefits(MD:3.2,P<0.001),lower perceived barriers(MD:-2.6,P=0.001),higher perceived susceptibility(MD:2.8,P=0.002),and higher perceived severity(MD:3.1,P<0.001)than the control group at six months.CONCLUSION Health education interventions based on the HBM effectively improve blood pressure control and health beliefs in patients with hypertension and should be implemented in clinical practice and community settings.

Predictive factors and model validation of post-colon polyp surgery Helicobacter pylori infection

BACKGROUND Recently,research has linked Helicobacter pylori(H.pylori)stomach infection to colonic inflammation,mediated by toxin production,potentially impacting colorectal cancer occurrence.AIM To investigate the risk factors for post-colon polyp surgery,H.pylori infection,and its correlation with pathologic type.METHODS Eighty patients who underwent colon polypectomy in our hospital between January 2019 and January 2023 were retrospectively chosen.They were then randomly split into modeling(n=56)and model validation(n=24)sets using R.The modeling cohort was divided into an H.pylori-infected group(n=37)and an H.pylori-uninfected group(n=19).Binary logistic regression analysis was used to analyze the factors influencing the occurrence of H.pylori infection after colon polyp surgery.A roadmap prediction model was established and validated.Finally,the correlation between the different pathological types of colon polyps and the occurrence of H.pylori infection was analyzed after colon polyp surgery.RESULTS Univariate results showed that age,body mass index(BMI),literacy,alcohol consumption,polyp pathology type,high-risk adenomas,and heavy diet were all influential factors in the development of H.pylori infection after intestinal polypectomy.Binary multifactorial logistic regression analysis showed that age,BMI,and type of polyp pathology were independent predictors of the occurrence of H.pylori infection after intestinal polypectomy.The area under the receiver operating characteristic curve was 0.969[95%confidence interval(95%CI):0.928–1.000]and 0.898(95%CI:0.773–1.000)in the modeling and validation sets,respectively.The slope of the calibration curve of the graph was close to 1,and the goodness-of-fit test was P>0.05 in the two sets.The decision analysis curve showed a high rate of return in both sets.The results of the correlation analysis between different pathological types and the occurrence of H.pylori infection after colon polyp surgery showed that hyperplastic polyps,inflammatory polyps,and the occurrence of H.pylori infection were not significantly correlated.In contrast,adenomatous polyps showed a significant positive correlation with the occurrence of H.pylori infection.CONCLUSION Age,BMI,and polyps of the adenomatous type were independent predictors of H.pylori infection after intestinal polypectomy.Moreover,the further constructed column-line graph prediction model of H.pylori infection after intestinal polypectomy showed good predictive ability.

The Model of“Post,Course,Competition,Certificate”Integrated Education for Electronic Information Engineering Technology Major in Higher Vocational Colleges

The core education function of higher vocational colleges is to train technical talents with high quality,so as to meet the needs of talents in the development stage of our society.Under the guidance of talent training,higher vocational colleges need to pay attention to establishing an all-round and three-dimensional education model,and promote innovation of higher vocational education on the basis of this.It is also a way to promote the innovation of higher vocational education to vigorously promote the construction of“post,course,competition,certificate”mode in the construction of education mode.Through the construction of“post,course,competition,certificate”mode,the education mode of higher vocational colleges is gradually improved,so as to strengthen the effectiveness of talent training in higher vocational colleges.Therefore,in this paper,the author puts forward some suggestions to promote the construction of the integrated education mode of the electronic information engineering technology major in higher vocational colleges,so as to help improve the talent training level of higher vocational colleges.

Use of machine learning models for the prognostication of liver transplantation: A systematic review

BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are pivotal in identifying the most suitable transplant candidates.Traditionally,scoring systems like the model for end-stage liver disease have been instrumental in this process.Nevertheless,the landscape of prognostication is undergoing a transformation with the integration of machine learning(ML)and artificial intelligence models.AIM To assess the utility of ML models in prognostication for LT,comparing their performance and reliability to established traditional scoring systems.METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines,we conducted a thorough and standardized literature search using the PubMed/MEDLINE database.Our search imposed no restrictions on publication year,age,or gender.Exclusion criteria encompassed non-English studies,review articles,case reports,conference papers,studies with missing data,or those exhibiting evident methodological flaws.RESULTS Our search yielded a total of 64 articles,with 23 meeting the inclusion criteria.Among the selected studies,60.8%originated from the United States and China combined.Only one pediatric study met the criteria.Notably,91%of the studies were published within the past five years.ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values(ranging from 0.6 to 1)across all studies,surpassing the performance of traditional scoring systems.Random forest exhibited superior predictive capabilities for 90-d mortality following LT,sepsis,and acute kidney injury(AKI).In contrast,gradient boosting excelled in predicting the risk of graft-versus-host disease,pneumonia,and AKI.CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT,marking a significant evolution in the field of prognostication.

Novel Investigation of Stochastic Fractional Differential Equations Measles Model via the White Noise and Global Derivative Operator Depending on Mittag-Leffler Kernel

Because of the features involved with their varied kernels,differential operators relying on convolution formulations have been acknowledged as effective mathematical resources for modeling real-world issues.In this paper,we constructed a stochastic fractional framework of measles spreading mechanisms with dual medication immunization considering the exponential decay and Mittag-Leffler kernels.In this approach,the overall population was separated into five cohorts.Furthermore,the descriptive behavior of the system was investigated,including prerequisites for the positivity of solutions,invariant domain of the solution,presence and stability of equilibrium points,and sensitivity analysis.We included a stochastic element in every cohort and employed linear growth and Lipschitz criteria to show the existence and uniqueness of solutions.Several numerical simulations for various fractional orders and randomization intensities are illustrated.