How completely are randomized controlled trials of non-pharmacological interventions following concussion reported? A systematic review

Purpose:The study aimed to examine the reporting completeness of randomized controlled trials(RCTs)of non-pharmacological interventions following concussion.Methods:We searched MEDLINE,Embase,PsycInfo,CINAHL,and Web of Science up to May 2022.Two reviewers independently screened studies and assessed reporting completeness using the Template for Intervention Description and Replication(TIDieR),Consensus on Exercise Reporting Template(CERT),and international Consensus on Therapeutic Exercise aNd Training(i-CONTENT)checklists.Additional information was sought my study authors where reporting was incomplete.Risk of bias(ROB)was assessed with the Cochrane ROB-2 Tool.RCTs examining non-pharmacological interventions following concussion.Results:We included 89 RCTs(n=53 high ROB)examining 11 different interventions for concussion:sub-symptom threshold aerobic exercise,cervicovestibular therapy,physical/cognitive rest,vision therapy,education,psychotherapy,hyperbaric oxygen therapy,transcranial magnetic stimulation,blue light therapy,osteopathic manipulation,and head/neck cooling.Median scores were:TIDieR 9/12(75%;interquartile range(IQR)=5;range:5-12),CERT 17/19(89%;IQR=2;range:10-19),and i-CONTENT 6/7(86%;IQR=1;range:5-7).Percentage of studies completely reporting all items was TIDieR 35%(31/89),CERT 24%(5/21),and i-CONTENT 10%(2/21).Studies were more completely reported after publication of TIDieR(t_(87)=2.08;p=0.04)and CERT(t_(19)=2.72;p=0.01).Reporting completeness was not strongly associated with journal impact factor(TIDieR:rs=0.27;p=0.01;CERT:r_(s)=-0.44;p=0.06;i-CONTENT:r_(s)=-0.17;p=0.48)or ROB(TIDieR:rs=0.11;p=0.31;CERT:rs=0.04;p=0.86;i-CONTENT:rs=0.12;p=0.60).Conclusion:RCTs of non-pharmacological interventions following concussion demonstrate moderate to good reporting completeness,but are often missing key components,particularly modifications,motivational strategies,and qualified supervisor.Reporting completeness improved after TIDieR and CERT publication,but publication in highly cited journals and low ROB do not guarantee reporting completeness.

Risk Factors of Depression Screened by Two-Sample Mendelian Randomization Analysis:A Systematic Review

Objective This study explored the potentially modifiable factors for depression and major depressive disorder(MDD)from the MR-Base database and further evaluated the associations between drug targets with MDD.Methods We analyzed two-sample of Mendelian randomization(2SMR)using genetic variant depression(n=113,154)and MDD(n=208,811)from Genome-Wide Association Studies(GWAS).Separate calculations were performed with modifiable risk factors from MR-Base for 1,001 genomes.The MR analysis was performed by screening drug targets with MDD in the DrugBank database to explore the therapeutic targets for MDD.Inverse variance weighted(IVW),fixed-effect inverse variance weighted(FE-IVW),MR-Egger,weighted median,and weighted mode were used for complementary calculation.Results The potential causal relationship between modifiable risk factors and depression contained 459 results for depression and 424 for MDD.Also,the associations between drug targets and MDD showed that SLC6A4,GRIN2A,GRIN2C,SCN10A,and IL1B expression are associated with an increased risk of depression.In contrast,ADRB1,CHRNA3,HTR3A,GSTP1,and GABRG2 genes are candidate protective factors against depression.Conclusion This study identified the risk factors causally associated with depression and MDD,and estimated 10 drug targets with significant impact on MDD,providing essential information for formulating strategies to prevent and treat depression.

An Optimized System of Random Forest Model by Global Harmony Search with Generalized Opposition-Based Learning for Forecasting TBM Advance Rate

As massive underground projects have become popular in dense urban cities,a problem has arisen:which model predicts the best for Tunnel Boring Machine(TBM)performance in these tunneling projects?However,performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers.On the other hand,a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule.The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications.The previously-proposed intelligent techniques in this field are mostly based on a single or base model with a low level of accuracy.Hence,this study aims to introduce a hybrid randomforest(RF)technique optimized by global harmony search with generalized oppositionbased learning(GOGHS)for forecasting TBM advance rate(AR).Optimizing the RF hyper-parameters in terms of,e.g.,tree number and maximum tree depth is the main objective of using the GOGHS-RF model.In the modelling of this study,a comprehensive databasewith themost influential parameters onTBMtogetherwithTBM AR were used as input and output variables,respectively.To examine the capability and power of the GOGHSRF model,three more hybrid models of particle swarm optimization-RF,genetic algorithm-RF and artificial bee colony-RF were also constructed to forecast TBM AR.Evaluation of the developed models was performed by calculating several performance indices,including determination coefficient(R2),root-mean-square-error(RMSE),and mean-absolute-percentage-error(MAPE).The results showed that theGOGHS-RF is a more accurate technique for estimatingTBMAR compared to the other applied models.The newly-developedGOGHS-RFmodel enjoyed R2=0.9937 and 0.9844,respectively,for train and test stages,which are higher than a pre-developed RF.Also,the importance of the input parameters was interpreted through the SHapley Additive exPlanations(SHAP)method,and it was found that thrust force per cutter is the most important variable on TBMAR.The GOGHS-RF model can be used in mechanized tunnel projects for predicting and checking performance.

GRU Enabled Intrusion Detection System for IoT Environment with Swarm Optimization and Gaussian Random Forest Classification

In recent years,machine learning(ML)and deep learning(DL)have significantly advanced intrusion detection systems,effectively addressing potential malicious attacks across networks.This paper introduces a robust method for detecting and categorizing attacks within the Internet of Things(IoT)environment,leveraging the NSL-KDD dataset.To achieve high accuracy,the authors used the feature extraction technique in combination with an autoencoder,integrated with a gated recurrent unit(GRU).Therefore,the accurate features are selected by using the cuckoo search algorithm integrated particle swarm optimization(PSO),and PSO has been employed for training the features.The final classification of features has been carried out by using the proposed RF-GNB random forest with the Gaussian Naïve Bayes classifier.The proposed model has been evaluated and its performance is verified with some of the standard metrics such as precision,accuracy rate,recall F1-score,etc.,and has been compared with different existing models.The generated results that detected approximately 99.87%of intrusions within the IoT environments,demonstrated the high performance of the proposed method.These results affirmed the efficacy of the proposed method in increasing the accuracy of intrusion detection within IoT network systems.

Seismic safety assessment with non-Gaussian random processes for train-bridge coupled systems

Extensive high-speed railway(HSR)network resembled the intricate vascular system of the human body,crisscrossing mainlands.Seismic events,known for their unpredictability,pose a significant threat to both trains and bridges,given the HSR’s extended operational duration.Therefore,ensuring the running safety of train-bridge coupled(TBC)system,primarily composed of simply supported beam bridges,is paramount.Traditional methods like the Monte Carlo method fall short in analyzing this intricate system efficiently.Instead,efficient algorithm like the new point estimate method combined with moment expansion approximation(NPEM-MEA)is applied to study random responses of numerical simulation TBC systems.Validation of the NPEM-MEA’s feasibility is conducted using the Monte Carlo method.Comparative analysis confirms the accuracy and efficiency of the method,with a recommended truncation order of four to six for the NPEM-MEA.Additionally,the influences of seismic magnitude and epicentral distance are discussed based on the random dynamic responses in the TBC system.This methodology not only facilitates seismic safety assessments for TBC systems but also contributes to standard-setting for these systems under earthquake conditions.

Association between renin-angiotensin system inhibitor and the risk of CMV pneumonia:a Mendelian randomization study

Background:Cytomegalovirus(CMV)reactivation is linked to a high mortality rate,especially among the elderly.Prior research suggests that renin-angiotensin system(RAS)inhibitors may influence both the onset and prognosis of pneumonia.This study aims to examine the causal relationship between RAS inhibitor use and the risk of CMV pneumonia using Mendelian randomization(MR)analysis.Methods:We conducted an analysis using data from two genome-wide association studies(GWAS)involving individuals of European ancestry.This dataset included individuals treated with RAS inhibitors and those with CMV pneumonia.We assessed the relationship between RAS inhibitor use and CMV pneumonia risk using the inverse variance weighted(IVW)method.The results were further evaluated for pleiotropy,heterogeneity,and robustness.Results:The Mendelian randomization(MR)analysis revealed a causal relationship between RAS inhibitor use and an increased risk of CMV pneumonia(IVW:odds ratio[OR]=2.73;95%confidence interval[CI]=1.11-6.73;P=0.028).Conclusions:Our finding indicate a positive causal relationship between the use of RAS inhibitors and the onset of CMV pneumonia.

Structural Damage Identification System Suitable for Old Arch Bridge in Rural Regions: Random Forest Approach

A huge number of old arch bridges located in rural regions are at the peak of maintenance.The health monitoring technology of the long-span bridge is hardly applicable to the small-span bridge,owing to the absence of technical resources and sufficient funds in rural regions.There is an urgent need for an economical,fast,and accurate damage identification solution.The authors proposed a damage identification system of an old arch bridge implemented with amachine learning algorithm,which took the vehicle-induced response as the excitation.A damage index was defined based on wavelet packet theory,and a machine learning sample database collecting the denoised response was constructed.Through comparing three machine learning algorithms:Back-Propagation Neural Network(BPNN),Support Vector Machine(SVM),and Random Forest(R.F.),the R.F.damage identification model were found to have a better recognition ability.Finally,the Particle Swarm Optimization(PSO)algorithm was used to optimize the number of subtrees and split features of the R.F.model.The PSO optimized R.F.model was capable of the identification of different damage levels of old arch bridges with sensitive damage index.The proposed framework is practical and promising for the old bridge’s structural damage identification in rural regions.

Theoretical and experimental study of a bi-stable piezoelectric energy harvester under hybrid galloping and band-limited random excitations

In the practical environment,it is very common for the simultaneous occurrence of base excitation and crosswind.Scavenging the combined energy of vibration and wind with a single energy harvesting structure is fascinating.For this purpose,the effects of the wind speed and random excitation level are investigated with the stochastic averaging method(SAM)based on the energy envelope.The results of the analytical prediction are verified with the Monte-Carlo method(MCM).The numerical simulation shows that the introduction of wind can reduce the critical excitation level for triggering an inter-well jump and make a bi-stable energy harvester(BEH)realize the performance enhancement for a weak base excitation.However,as the strength of the wind increases to a particular level,the influence of the random base excitation on the dynamic responses is weakened,and the system exhibits a periodic galloping response.A comparison between a BEH and a linear energy harvester(LEH)indicates that the BEH demonstrates inferior performance for high-speed wind.Relevant experiments are conducted to investigate the validity of the theoretical prediction and numerical simulation.The experimental findings also show that strong random excitation is favorable for the BEH in the range of low wind speeds.However,as the speed of the incoming wind is up to a particular level,the disadvantage of the BEH becomes clear and evident.

Semi-Global Practical Stability of Random Systems and Its Application

In this paper,a new stochastic analysis tool on semi-global stability is constructed,for nonlinear systems disturbed by stochastic processes with strongly bounded in probability.The definition of semi-global noise to state practical stability in probability and its Lyapunov criterion for random systems are presented.As a major application of stability,the semi-global practical tracking of random nonlinear systems based on dynamic surface control technique is considered.The trajectory tracking of manipulator robot driven by direct current motors is carried out in simulation to illustrate the effectiveness and feasibility of the control scheme.

Immune cell signatures and causal association with irritable bowel syndrome:A mendelian randomization study

BACKGROUND The mucosal barrier's immune-brain interactions,pivotal for neural development and function,are increasingly recognized for their potential causal and therapeutic relevance to irritable bowel syndrome(IBS).Prior studies linking immune inflammation with IBS have been inconsistent.To further elucidate this relationship,we conducted a Mendelian randomization(MR)analysis of 731 immune cell markers to dissect the influence of various immune phenotypes on IBS.Our goal was to deepen our understanding of the disrupted brain-gut axis in IBS and to identify novel therapeutic targets.AIM To leverage publicly available data to perform MR analysis on 731 immune cell markers and explore their impact on IBS.We aimed to uncover immunophenotypic associations with IBS that could inform future drug development and therapeutic strategies.METHODS We performed a comprehensive two-sample MR analysis to evaluate the causal relationship between immune cell markers and IBS.By utilizing genetic data from public databases,we examined the causal associations between 731 immune cell markers,encompassing median fluorescence intensity,relative cell abundance,absolute cell count,and morphological parameters,with IBS susceptibility.Sensitivity analyses were conducted to validate our findings and address potential heterogeneity and pleiotropy.RESULTS Bidirectional false discovery rate correction indicated no significant influence of IBS on immunophenotypes.However,our analysis revealed a causal impact of IBS on 30 out of 731 immune phenotypes(P<0.05).Nine immune phenotypes demonstrated a protective effect against IBS[inverse variance weighting(IVW)<0.05,odd ratio(OR)<1],while 21 others were associated with an increased risk of IBS onset(IVW≥0.05,OR≥1).CONCLUSION Our findings underscore a substantial genetic correlation between immune cell phenotypes and IBS,providing valuable insights into the pathophysiology of the condition.These results pave the way for the development of more precise biomarkers and targeted therapies for IBS.Furthermore,this research enriches our comprehension of immune cell roles in IBS pathogenesis,offering a foundation for more effective,personalized treatment approaches.These advancements hold promise for improving IBS patient quality of life and reducing the disease burden on individuals and their families.

Causal associations between gastroesophageal reflux disease and essential hypertension: A bidirectional Mendelian randomization study

BACKGROUND Clinical studies have reported that patients with gastroesophageal reflux disease(GERD)have a higher prevalence of hypertension.AIM To performed a bidirectional Mendelian randomization(MR)analysis to investi-gate the causal link between GERD and essential hypertension.METHODS Eligible single nucleotide polymorphisms(SNPs)were selected,and weighted median,inverse variance weighted(IVW)as well as MR egger(MR-Egger)re-gression were used to examine the potential causal association between GERD and hypertension.The MR-Pleiotropy RESidual Sum and Outlier analysis was used to detect and attempt to reduce horizontal pleiotropy by removing outliers SNPs.The MR-Egger intercept test,Cochran’s Q test and“leave-one-out”sen-sitivity analysis were performed to evaluate the horizontal pleiotropy,heterogen-eities,and stability of single instrumental variable.RESULTS IVW analysis exhibited an increased risk of hypertension(OR=1.46,95%CI:1.33-1.59,P=2.14E-16)in GERD patients.And the same result was obtained in replication practice(OR=1.002,95%CI:1.0008-1.003,P=0.000498).Meanwhile,the IVW analysis showed an increased risk of systolic blood pressure(β=0.78,95%CI:0.11-1.44,P=0.021)and hypertensive heart disease(OR=1.68,95%CI:1.36-2.08,P=0.0000016)in GERD patients.Moreover,we found an decreased risk of Barrett's esophagus(OR=0.91,95%CI:0.83-0.99,P=0.043)in essential hypertension patients.CONCLUSION We found that GERD would increase the risk of essential hypertension,which provided a novel prevent and therapeutic perspectives of essential hypertension.

Identification of Mixtures of Two Types of Body Fluids Using the Multiplex Methylation System and Random Forest Models

Objective Body fluid mixtures are complex biological samples that frequently occur in crime scenes,and can provide important clues for criminal case analysis.DNA methylation assay has been applied in the identification of human body fluids,and has exhibited excellent performance in predicting single-source body fluids.The present study aims to develop a methylation SNaPshot multiplex system for body fluid identification,and accurately predict the mixture samples.In addition,the value of DNA methylation in the prediction of body fluid mixtures was further explored.Methods In the present study,420 samples of body fluid mixtures and 250 samples of single body fluids were tested using an optimized multiplex methylation system.Each kind of body fluid sample presented the specific methylation profiles of the 10 markers.Results Significant differences in methylation levels were observed between the mixtures and single body fluids.For all kinds of mixtures,the Spearman’s correlation analysis revealed a significantly strong correlation between the methylation levels and component proportions(1:20,1:10,1:5,1:1,5:1,10:1 and 20:1).Two random forest classification models were trained for the prediction of mixture types and the prediction of the mixture proportion of 2 components,based on the methylation levels of 10 markers.For the mixture prediction,Model-1 presented outstanding prediction accuracy,which reached up to 99.3%in 427 training samples,and had a remarkable accuracy of 100%in 243 independent test samples.For the mixture proportion prediction,Model-2 demonstrated an excellent accuracy of 98.8%in 252 training samples,and 98.2%in 168 independent test samples.The total prediction accuracy reached 99.3%for body fluid mixtures and 98.6%for the mixture proportions.Conclusion These results indicate the excellent capability and powerful value of the multiplex methylation system in the identification of forensic body fluid mixtures.

Distributed dynamic event-based finite-time dissipative synchronization control for semi-Markov switched fuzzy cyber-physical systems against random packet losses

This paper is concerned with the finite-time dissipative synchronization control problem of semi-Markov switched cyber-physical systems in the presence of packet losses, which is constructed by the Takagi–Sugeno fuzzy model. To save the network communication burden, a distributed dynamic event-triggered mechanism is developed to restrain the information update. Besides, random packet dropouts following the Bernoulli distribution are assumed to occur in sensor to controller channels, where the triggered control input is analyzed via an equivalent method containing a new stochastic variable. By establishing the mode-dependent Lyapunov–Krasovskii functional with augmented terms, the finite-time boundness of the error system limited to strict dissipativity is studied. As a result of the help of an extended reciprocally convex matrix inequality technique, less conservative criteria in terms of linear matrix inequalities are deduced to calculate the desired control gains. Finally, two examples in regard to practical systems are provided to display the effectiveness of the proposed theory.

Genetically predicted fatty liver disease and risk of psychiatric disorders: A mendelian randomization study

BACKGROUND Non-alcoholic fatty liver disease(NAFLD)and alcohol-related liver disease(Ar-LD)constitute the primary forms of chronic liver disease,and their incidence is progressively increasing with changes in lifestyle habits.Earlier studies have do-cumented a correlation between the occurrence and development of prevalent mental disorders and fatty liver.AIM To investigate the correlation between fatty liver and mental disorders,thus ne-cessitating the implementation of a mendelian randomization(MR)study to elu-cidate this association.METHODS Data on NAFLD and ArLD were retrieved from the genome-wide association studies catalog,while information on mental disorders,including Alzheimer's disease,schizophrenia,anxiety disorder,attention deficit hyperactivity disorder(ADHD),bipolar disorder,major depressive disorder,multiple personality dis-order,obsessive-compulsive disorder(OCD),post-traumatic stress disorder(PTSD),and schizophrenia was acquired from the psychiatric genomics consor-tium.A two-sample MR method was applied to investigate mediators in signifi-cant associations.RESULTS After excluding weak instrumental variables,a causal relationship was identified between fatty liver disease and the occurrence and development of some psychia-tric disorders.Specifically,the findings indicated that ArLD was associated with a significantly elevated risk of developing ADHD(OR:5.81,95%CI:5.59-6.03,P<0.01),bipolar disorder(OR:5.73,95%CI:5.42-6.05,P=0.03),OCD(OR:6.42,95%CI:5.60-7.36,P<0.01),and PTSD(OR:5.66,95%CI:5.33-6.01,P<0.01).Meanwhile,NAFLD significantly increased the risk of developing bipolar disorder(OR:55.08,95%CI:3.59-845.51,P<0.01),OCD(OR:61.50,95%CI:6.69-565.45,P<0.01),and PTSD(OR:52.09,95%CI:4.24-639.32,P<0.01).CONCLUSION Associations were found between genetic predisposition to fatty liver disease and an increased risk of a broad range of psychiatric disorders,namely bipolar disorder,OCD,and PTSD,highlighting the significance of preven-tive measures against psychiatric disorders in patients with fatty liver disease.

Traditional Chinese medicine for acute mountain sickness prevention: A systematic review and meta-analysis of randomized controlled trials

Objective: To evaluate the efficacy of traditional Chinese medicine(TCM) for preventing acute mountain sickness(AMS).Methods: We included randomized controlled trials(RCTs) which evalueded the effect of TCM for preventing AMS, compared with a placebo, no treatment or acetazolamide. The literature was searched in 6major databases. RevMan 5.4 software was used for the meta-analysis. The relative risk for discrete variables and the mean difference for continuous variables with 95% confidence intervals(CIs) were applied to express the effect size. The risk of bias in the included studies was evaluated using the Cochrane risk assessment tool 2.0(RoB 2.0), and the evidence certainty was assessed using the Grading of Recommendations Assessment and the Development and Evaluation(GRADE) approach.Results: Twenty RCTs involving 3015 participants and 16 TCM patent drugs were included. The overall risk of bias in the majority of studies(15/20) was of some concerns. In terms of the AMS incidence,Rhodiola rosea(R. rosea, Hong Jing Tian) and Ginkgo biloba(G. biloba, Yin Xing Ye) were equivalent to the placebo/no treatment [RR(95% CI): 0.66(0.43-1.01), 0.82(0.63-1.06), respectively]. The AMS incidence in the G. biloba group was higher than that in the acetazolamide group [RR(95% CI): 2.92(1.69-5.06)]. In terms of improving the AMS symptom score on days 1 and 3 in the plateau, R. rosea and G. biloba were superior to the placebo or no treatment [MD(95% CI):-0.98(-1.71,-0.25),-2.05(-3.14,-0.95), respectively]. The other 14 Chinese patent medicines were evaluated in a single trial, and the majority of the results were negative. The subgroup analysis showed that the effect of R. rosea was related to the intervention time, way of ascending, and altitude.Conclusion: R. rosea and G. biloba were effective in improving AMS symptoms but had no effect in reducing the AMS incidence. There was insufficient evidence to support the use of other TCM patent drugs to prevent AMS. More randomized double-blind placebo-controlled trials are warranted to evaluate and screen effective Chinese patent medicines for AMS prevention.

Random vibration of hysteretic systems under Poisson white noise excitations

Hysteresis widely exists in civil structures,and dissipates the mechanical energy of systems.Research on the random vibration of hysteretic systems,however,is still insufficient,particularly when the excitation is non-Gaussian.In this paper,the radial basis function(RBF)neural network(RBF-NN)method is adopted as a numerical method to investigate the random vibration of the Bouc-Wen hysteretic system under the Poisson white noise excitations.The solution to the reduced generalized Fokker-PlanckKolmogorov(GFPK)equation is expressed in terms of the RBF-NNs with the Gaussian activation functions,whose weights are determined by minimizing the loss function of the reduced GFPK equation residual and constraint associated with the normalization condition.A steel fiber reinforced ceramsite concrete(SFRCC)column loaded by the Poisson white noise is studied as an example to illustrate the solution process.The effects of several important parameters of both the system and the excitation on the stochastic response are evaluated,and the obtained results are compared with those obtained by the Monte Carlo simulations(MCSs).The numerical results show that the RBF-NN method can accurately predict the stationary response with a considerable high computational efficiency.

Current status of outcome reporting in randomized controlled trials of traditional Chinese medicine for mammary gland hyperplasia: A systematic review

Objective:To assess outcome indicators in clinical trials and provide a reference for establishing a core outcome set to treat hyperplasia of mammary gland(HMG)with traditional Chinese medicine(TCM).Methods:Eight online databases were searched from their inception to December 31,2022,to assess outcomes reported in randomized controlled trials(RCTs)of HMG treated with TCM.The quality of the included studies was assessed according to the Cochrane Risk of Bias Assessment Tool.All outcomes were extracted,classified,and described.Results:A total of 8249 articles were initially retrieved.Of these,70 articles were eligible and involved 10618 participants with HMG.A total of 17 outcome indicators with a frequency of 271 times were involved and were collected according to six outcome domains.Conclusions:The core outcomes of RCTs of HMG treated with TCM are large and divergent.There are problems in evaluation standards,primary and secondary outcomes,TCM characteristic indicators,long-term prognosis,and standardization of reporting.It is recommended to strengthen the trial design and actively construct the core outcome sets with TCM characteristics for HMG.

Holistic conditions after colon cancer:A randomized controlled trial of systematic holistic care vs primary care

BACKGROUND Study showed that systemic holistic care not only aids in disease treatment and physical recovery to a certain extent but also effectively enhances patient psychological well-being,social support,and overall quality of life(QoL).AIM To assess systematic holistic care impact on the recovery and well-being of postoperative patients with colon cancer.METHODS Our randomized controlled trial included 98 postoperative patients with colon cancer admitted to our hospital from June 2021 to June 2022.Patients were divided into control and study groups.The control group received conventional postoperative nursing care,whereas the study group received systematic holistic nursing care.We monitored gastrointestinal function recovery,and recorded changes in serum albumin(ALB),prealbumin(PA),psychological state,selfmanagement,self-efficacy,QoL,and the occurrence of complications in patients before,at discharge,and 2 wk post-discharge.Spearman analysis assessed correlations between psychological state,self-management,self-efficacy,and QoL of patients in the study group 2 wk post-discharge.RESULTS Following the nursing intervention,we observed significantly shorter postoperative bowel sound recovery time,anal exhaust time,and defecation time in the study group than in the control group(P<0.05).Patient ALB and PA levels,psychological status,self-management ability,self-efficacy and QoL at discharge and 2 wk post-discharge significantly improved,with greater improvements observed in the study group(P<0.05).Both groups experienced complications post-interventions,but the intervention group had significantly lower complication rate(3/49,6.12%)(P<0.05).In the study group,patient anxiety,depression,self-management and QoL scores at 2 wk post-discharge exhibited a significant negative correlation(3/49,6.12%)with QoL scores,with correlation coefficients of r=-0.273,-0.522,-0.344,and P<0.01,respectively.Conversely,patient self-efficacy scores 2 wk postdischarge showed a positive correlation with QoL scores(r=0.410,P=0.000).CONCLUSION Systemic holistic nursing significantly benefits postoperative patients with colon cancer by promoting gastrointestinal recovery,improving post-operation well-being,reducing complications,and enhancing QoL.

一种基于KMeans与Random Forest的异常温升捕捉方法

针对线路老化、线路过载的火灾频发问题,分析了线路老化、线路过载与异常温升之间的关联性,以电流值、线缆温度作为输入,利用KMeans聚类算法划分可能存在异常温升的区间,通过Random Forest算法识别线路过载问题,可以提前通知用户整改线路,预防火灾的发生。

Python语言random库经典案例教学

Python是一种计算生态语言,也是开源的语言,具有丰富和强大的类库,语法简洁、清晰明了。随着物联网、大数据、人工智能等技术的兴起,Python位于PYPL全球编程语言排行榜的榜首,成为受人们欢迎的编程语言。主要阐述利用Python的random库解决一些实际问题,既提高学习者的兴趣和积极性,又能提高学习者利用所学知识分析和处理实际问题的能力。