Machine learning as a tool predicting short-term postoperative complications in Crohn’s disease patients undergoing intestinal resection: What frontiers?

The recent study,“Predicting short-term major postoperative complications in intestinal resection for Crohn’s disease:A machine learning-based study”invest-igated the predictive efficacy of a machine learning model for major postoperative complications within 30 days of surgery in Crohn’s disease(CD)patients.Em-ploying a random forest analysis and Shapley Additive Explanations,the study prioritizes factors such as preoperative nutritional status,operative time,and CD activity index.Despite the retrospective design’s limitations,the model’s robu-stness,with area under the curve values surpassing 0.8,highlights its clinical potential.The findings align with literature supporting preoperative nutritional therapy in inflammatory bowel diseases,emphasizing the importance of compre-hensive assessment and optimization.While a significant advancement,further research is crucial for refining preoperative strategies in CD patients.

An Innovative Approach to Predicting Scour Depth Around Foundations Under Combined Waves and Currents in Large-Scale Tests Based on Small-Scale Tests

This study presents an innovative theoretical approach to predicting the scour depth around a foundation in large-scale model tests based on small-scale model tests under combined waves and currents.In the present approach,the hydrodynamic parameters were designed based on the Froude similitude criteria.To avoid the cohesive behavior,we scaled the sediment size based on the settling velocity similarity,i.e.,the suspended load similarity.Then,a series of different scale model tests was conducted to obtain the scour depth around the pile in combined waves and currents.The fitting formula of scour depth from the small-scale model tests was used to predict the results of large-scale tests.The accuracy of the present approach was validated by comparing the prediction values with experimental data of large-scale tests.Moreover,the correctness and accuracy of the present approach for foundations with complex shapes,e.g.,the tripod foundation,was further checked.The results indicated that the fitting line from small-scale model tests slightly overestimated the experimental data of large-scale model tests,and the errors can be accepted.In general,the present approach was applied to predict the maximum or equilibrium scour depth of the large-scale model tests around single piles and tripods.

Failure characterization of fully grouted rock bolts under triaxial testing

Confining stresses serve as a pivotal determinant in shaping the behavior of grouted rock bolts.Nonetheless,prior investigations have oversimplified the three-dimensional stress state,primarily assuming hydrostatic stress conditions.Under these conditions,it is assumed that the intermediate principal stress(σ_(2))equals the minimum principal stress(σ_(3)).This assumption overlooks the potential variations in magnitudes of in situ stress conditions along all three directions near an underground opening where a rock bolt is installed.In this study,a series of push tests was meticulously conducted under triaxial conditions.These tests involved applying non-uniform confining stresses(σ_(2)≠σ_(3))to cubic specimens,aiming to unveil the previously overlooked influence of intermediate principal stresses on the strength properties of rock bolts.The results show that as the confining stresses increase from zero to higher levels,the pre-failure behavior changes from linear to nonlinear forms,resulting in an increase in initial stiffness from 2.08 kN/mm to 32.51 kN/mm.The load-displacement curves further illuminate distinct post-failure behavior at elevated levels of confining stresses,characterized by enhanced stiffness.Notably,the peak load capacity ranged from 27.9 kN to 46.5 kN as confining stresses advanced from σ_(2)=σ_(3)=0 to σ_(2)=20 MPa and σ_(3)=10 MPa.Additionally,the outcomes highlight an influence of confining stress on the lateral deformation of samples.Lower levels of confinement prompt overall dilation in lateral deformation,while higher confinements maintain a state of shrinkage.Furthermore,diverse failure modes have been identified,intricately tied to the arrangement of confining stresses.Lower confinements tend to induce a splitting mode of failure,whereas higher loads bring about a shift towards a pure interfacial shear-off and shear-crushed failure mechanism.

Superiority of a Convolutional Neural Network Model over Dynamical Models in Predicting Central Pacific ENSO

The application of deep learning is fast developing in climate prediction,in which El Ni?o–Southern Oscillation(ENSO),as the most dominant disaster-causing climate event,is a key target.Previous studies have shown that deep learning methods possess a certain level of superiority in predicting ENSO indices.The present study develops a deep learning model for predicting the spatial pattern of sea surface temperature anomalies(SSTAs)in the equatorial Pacific by training a convolutional neural network(CNN)model with historical simulations from CMIP6 models.Compared with dynamical models,the CNN model has higher skill in predicting the SSTAs in the equatorial western-central Pacific,but not in the eastern Pacific.The CNN model can successfully capture the small-scale precursors in the initial SSTAs for the development of central Pacific ENSO to distinguish the spatial mode up to a lead time of seven months.A fusion model combining the predictions of the CNN model and the dynamical models achieves higher skill than each of them for both central and eastern Pacific ENSO.

Unbound^(28)O,the heaviest oxygen isotope observed:a cutting-edge probe for testing nuclear models

The beyond-dripline oxygen isotopes^(27,28)O were recently observed at RIKEN,and were found to be unbound decaying into^(24)O by emitting neutrons.The unbound feature of the heaviest oxygen isotope,^(28)O,provides an excellent test for stateof-the-art nuclear models.The atomic nucleus is a self-organized quantum manybody system comprising specific numbers of protons Z and neutrons N.

Computed tomography radiomic features and clinical factors predicting the response to first transarterial chemoembolization in intermediate-stage hepatocellular carcinoma

Background:According to clinical practice guidelines,transarterial chemoembolization(TACE)is the standard treatment modality for patients with intermediate-stage hepatocellular carcinoma(HCC).Early prediction of treatment response can help patients choose a reasonable treatment plan.This study aimed to investigate the value of the radiomic-clinical model in predicting the efficacy of the first TACE treatment for HCC to prolong patient survival.Methods:A total of 164 patients with HCC who underwent the first TACE from January 2017 to September 2021 were analyzed.The tumor response was assessed by modified response evaluation criteria in solid tumors(mRECIST),and the response of the first TACE to each session and its correlation with overall survival were evaluated.The radiomic signatures associated with the treatment response were identified by the least absolute shrinkage and selection operator(LASSO),and four machine learning models were built with different types of regions of interest(ROIs)(tumor and corresponding tissues)and the model with the best performance was selected.The predictive performance was assessed with receiver operating characteristic(ROC)curves and calibration curves.Results:Of all the models,the random forest(RF)model with peritumor(+10 mm)radiomic signatures had the best performance[area under ROC curve(AUC)=0.964 in the training cohort,AUC=0.949 in the validation cohort].The RF model was used to calculate the radiomic score(Rad-score),and the optimal cutoff value(0.34)was calculated according to the Youden’s index.Patients were then divided into a high-risk group(Rad-score>0.34)and a low-risk group(Rad-score≤0.34),and a nomogram model was successfully established to predict treatment response.The predicted treatment response also allowed for significant discrimination of Kaplan-Meier curves.Multivariate Cox regression identified six independent prognostic factors for overall survival,including male[hazard ratio(HR)=0.500,95%confidence interval(CI):0.260–0.962,P=0.038],alpha-fetoprotein(HR=1.003,95%CI:1.002–1.004,P<0.001),alanine aminotransferase(HR=1.003,95%CI:1.001–1.005,P=0.025),performance status(HR=2.400,95%CI:1.200–4.800,P=0.013),the number of TACE sessions(HR=0.870,95%CI:0.780–0.970,P=0.012)and Rad-score(HR=3.480,95%CI:1.416–8.552,P=0.007).Conclusions:The radiomic signatures and clinical factors can be well-used to predict the response of HCC patients to the first TACE and may help identify the patients most likely to benefit from TACE.

Validation and performance of three scoring systems for predicting primary non-function and early allograft failure after liver transplantation

Background: Primary non-function(PNF) and early allograft failure(EAF) after liver transplantation(LT) seriously affect patient outcomes. In clinical practice, effective prognostic tools for early identifying recipients at high risk of PNF and EAF were urgently needed. Recently, the Model for Early Allograft Function(MEAF), PNF score by King's College(King-PNF) and Balance-and-Risk-Lactate(BAR-Lac) score were developed to assess the risks of PNF and EAF. This study aimed to externally validate and compare the prognostic performance of these three scores for predicting PNF and EAF. Methods: A retrospective study included 720 patients with primary LT between January 2015 and December 2020. MEAF, King-PNF and BAR-Lac scores were compared using receiver operating characteristic(ROC) and the net reclassification improvement(NRI) and integrated discrimination improvement(IDI) analyses. Results: Of all 720 patients, 28(3.9%) developed PNF and 67(9.3%) developed EAF in 3 months. The overall early allograft dysfunction(EAD) rate was 39.0%. The 3-month patient mortality was 8.6% while 1-year graft-failure-free survival was 89.2%. The median MEAF, King-PNF and BAR-Lac scores were 5.0(3.5–6.3),-2.1(-2.6 to-1.2), and 5.0(2.0–11.0), respectively. For predicting PNF, MEAF and King-PNF scores had excellent area under curves(AUCs) of 0.872 and 0.891, superior to BAR-Lac(AUC = 0.830). The NRI and IDI analyses confirmed that King-PNF score had the best performance in predicting PNF while MEAF served as a better predictor of EAD. The EAF risk curve and 1-year graft-failure-free survival curve showed that King-PNF was superior to MEAF and BAR-Lac scores for stratifying the risk of EAF. Conclusions: MEAF, King-PNF and BAR-Lac were validated as practical and effective risk assessment tools of PNF. King-PNF score outperformed MEAF and BAR-Lac in predicting PNF and EAF within 6 months. BAR-Lac score had a huge advantage in the prediction for PNF without post-transplant variables. Proper use of these scores will help early identify PNF, standardize grading of EAF and reasonably select clinical endpoints in relative studies.

Electrochemical biosensors for point-of-care testing

Point-of-care testing(POCT)is the practice of diagnosing and monitoring diseases where the patient is located,as opposed to traditional treatment conducted solely in a medical laboratory or other clinical setting.POCT has been less common in the recent past due to a lack of portable medical devices capable of facilitating effective medical testing.However,recent growth has occurred in this field due to advances in diagnostic technologies,device miniaturization,and progress in wearable electronics.Among these developments,electrochemical sensors have attracted interest in the POCT field due to their high sensitivity,compact size,and affordability.They are used in various applications,from disease diagnosis to health status monitoring.In this paper we explore recent advancements in electrochemical sensors,the methods of fabricating them,and the various types of sensing mechanisms that can be used.Furthermore,we delve into methods for immobilizing specific biorecognition elements,including enzymes,antibodies,and aptamers,onto electrode surfaces and how these sensors are used in real-world POCT settings.

Aquifer Test求群孔抽水试验水文地质参数

在水文地质条件复杂的地区,往往采用大型的群孔抽水试验充分揭露含水层特征,获取水文地质参数。用Aquifer Test软件求解水文地质参数,则方便实用、简捷高效,可快速输出拟合图形和求参结果。Aquifer Test提供了丰富的求参方案,求参必须结合当地当时的水文地质条件实际情况考虑,选择适合的方案和时空数据求取水文地质参数。

Seismic performance evaluation of water supply pipes installed in a full-scale RC frame structure based on a shaking table test

As an important part of nonstructural components,the seismic response of indoor water supply pipes deserves much attention.This paper presents shaking table test research on water supply pipes installed in a full-scale reinforced concrete(RC)frame structure.Different material pipes and different methods for penetrating the reinforced concrete floors are combined to evaluate the difference in seismic performance.Floor response spectra and pipe acceleration amplification factors based on test data are discussed and compared with code provisions.A seismic fragility study of displacement demand is conducted based on numerical simulation.The acceleration response and displacement response of different combinations are compared.The results show that the combination of different pipe materials and different passing-through methods can cause obvious differences in the seismic response of indoor riser pipes.

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.

An enhanced method for predicting and analysing forest fires using an attention-based CNN model

Prediction,prevention,and control of forest fires are crucial on at all scales.Developing effective fire detection systems can aid in their control.This study proposes a novel CNN(convolutional neural network)using an attention blocks module which combines an attention module with numerous input layers to enhance the performance of neural networks.The suggested model focuses on predicting the damage affected/burned areas due to possible wildfires and evaluating the multilateral interactions between the pertinent factors.The results show the impacts of CNN using attention blocks for feature extraction and to better understand how ecosystems are affected by meteorological factors.For selected meteorological data,RMSE 12.08 and MAE 7.45 values provide higher predictive power for selecting relevant and necessary features to provide optimal performance with less operational and computational costs.These findings show that the suggested strategy is reliable and effective for planning and managing fire-prone regions as well as for predicting forest fire damage.

Development of multi-functional anchorage support dynamic-static coupling performance test system and its application

In underground engineering with complex conditions,the bolt(cable)anchorage support system is in an environment where static and dynamic stresses coexist,under the action of geological conditions such as high stresses and strong disturbances and construction conditions such as the application of high prestress.It is essential to study the support components performance under dynamic-static coupling conditions.Based on this,a multi-functional anchorage support dynamic-static coupling performance test system(MAC system)is developed,which can achieve 7 types of testing functions,including single component performance,anchored net performance,anchored rock performance and so on.The bolt and cable mechanical tests are conducted by MAC system under different prestress levels.The results showed that compared to the non-prestress condition,the impact resistance performance of prestressed bolts(cables)is significantly reduced.In the prestress range of 50–160 k N,the maximum reduction rate of impact energy resisted by different types of bolts is 53.9%–61.5%compared to non-prestress condition.In the prestress range of 150–300 k N,the impact energy resisted by high-strength cable is reduced by76.8%–84.6%compared to non-prestress condition.The MAC system achieves dynamic-static coupling performance test,which provide an effective means for the design of anchorage support system.

Inflow and outflow permeability tests in a very soft clay under low stresses

In situ inflow and outflow permeability tests with the BAT probe at SarapuíII soft clay test site are presented.A description of the BAT permeability test is provided,discussing its advantages and shortcomings,especially in the case of very soft clays under low stresses.Pore pressures were monitored during probe installation and were found to be slightly lower than piezocone u2 pore pressures,consistent with the position of the filter.The role of filter tip saturation was investigated after the usual saturation procedure provided an unsatisfactory pore pressure response during probe installation.Results show that the vacuum saturation procedure provides adequate response during installation and increases the reliability of the coefficient of permeability determination in early measurements.Both inflow and outflow tests yielded similar results,indicating that careful execution of the test can lead to good test repeatability regardless of the loading condition.Various sequences of alternated inflow and outflow tests have yielded similar results,indicating that soil reconsolidation and filter clogging were negligible in the tests performed.Data are presented concerning the relationship between index parameters and the in situ coefficient of permeability for SarapuíII clay,which plot outside the range of existing databases.

C-CORE:Clustering by Code Representation to Prioritize Test Cases in Compiler Testing

Edge devices,due to their limited computational and storage resources,often require the use of compilers for program optimization.Therefore,ensuring the security and reliability of these compilers is of paramount importance in the emerging field of edge AI.One widely used testing method for this purpose is fuzz testing,which detects bugs by inputting random test cases into the target program.However,this process consumes significant time and resources.To improve the efficiency of compiler fuzz testing,it is common practice to utilize test case prioritization techniques.Some researchers use machine learning to predict the code coverage of test cases,aiming to maximize the test capability for the target compiler by increasing the overall predicted coverage of the test cases.Nevertheless,these methods can only forecast the code coverage of the compiler at a specific optimization level,potentially missing many optimization-related bugs.In this paper,we introduce C-CORE(short for Clustering by Code Representation),the first framework to prioritize test cases according to their code representations,which are derived directly from the source codes.This approach avoids being limited to specific compiler states and extends to a broader range of compiler bugs.Specifically,we first train a scaled pre-trained programming language model to capture as many common features as possible from the test cases generated by a fuzzer.Using this pre-trained model,we then train two downstream models:one for predicting the likelihood of triggering a bug and another for identifying code representations associated with bugs.Subsequently,we cluster the test cases according to their code representations and select the highest-scoring test case from each cluster as the high-quality test case.This reduction in redundant testing cases leads to time savings.Comprehensive evaluation results reveal that code representations are better at distinguishing test capabilities,and C-CORE significantly enhances testing efficiency.Across four datasets,C-CORE increases the average of the percentage of faults detected(APFD)value by 0.16 to 0.31 and reduces test time by over 50% in 46% of cases.When compared to the best results from approaches using predicted code coverage,C-CORE improves the APFD value by 1.1% to 12.3% and achieves an overall time-saving of 159.1%.

Real-world utility of serological tests in patients with suspected scrub typhus in the Republic of Korea:A single-center,retrospective,observational study

Objective:Serological tests are widely used for scrub typhus diagnosis;however,their limitations are evident.This study aims to assess their practical value in clinical settings.Methods:We analyzed the data of adult patients with suspected scrub typhus who visited a tertiary care hospital in the Republic of Korea from September to December from 2019 to 2021.The included patients had an acute fever and at least one of the following ten secondary findings:myalgia,skin rash,eschar,headache,thrombocytopenia,increased liver enzyme levels,lymphadenopathy,hepatomegaly,splenomegaly,and pleural effusion.The diagnoses were grouped as scrub typhus or other diseases by two infectious disease physicians.Results:Among 136 patients who met the eligibility criteria,109 had scrub typhus and 27 had different diseases.Single and paired total antibodies using immunofluorescence assay(IFA),and total antibodies using immunochromatography-based rapid diagnostic testing(ICT)were measured in 98%,22%,and 75%of all patients,respectively.Confirmation using paired samples for scrub typhus was established at a median of 11[interquartile range(IQR)10-16]days following the first visit.Among the 82 admitted patients,the median admission time was 9(IQR 7-13)days.According to IFA,58(55%)patients with scrub typhus had total immunoglobulin titers≥1:320,while 23(85%)patients with other disease had titers<1:320.Positive ICT results were observed in 64(74%)patients with scrub typhus and 10(67%)patients with other diseases showed negative ICT results.Conclusions:Serological testing for scrub typhus is currently insufficient for decision-making in clinical practice.

Evaluation of urea breath test as a diagnostic tool for Helicobacter pylori infection in adult dyspeptic patients

In this editorial,we discuss the article in the World Journal of Gastroenterology.The article conducts a meta-analysis of the diagnostic accuracy of the urea breath test(UBT),a non-invasive method for detecting Helicobacter pylori(H.pylori)infection in humans.It is based on radionuclide-labeled urea.Various methods,both invasive and non-invasive,are available for diagnosing H.pylori infection,inclu-ding endoscopy with biopsy,serology for immunoglobulin titers,stool antigen analysis,and UBT.Several guidelines recommend UBTs as the primary choice for diagnosing H.pylori infection and for reexamining after eradication therapy.It is used to be the first choice non-invasive test due to their high accuracy,specificity,rapid results,and simplicity.Moreover,its performance remains unaffected by the distribution of H.pylori in the stomach,allowing a high flow of patients to be tested.Despite its widespread use,the performance characteristics of UBT have been inconsistently described and remain incompletely defined.There are two UBTs available with Food and Drug Administration approval:The 13C and 14C tests.Both tests are affordable and can provide real-time results.Physicians may prefer the 13C test because it is non-radioactive,compared to 14C which uses a radioactive isotope,especially in young children and pregnant women.Although there was heterogeneity among the studies regarding the diagnostic accuracy of both UBTs,13C-UBT consistently outperforms the 14C-UBT.This makes the 13C-UBT the preferred diagnostic approach.Furthermore,the provided findings of the meta-analysis emphasize the significance of precise considerations when choosing urea dosage,assessment timing,and measurement techniques for both the 13C-UBT and 14C-UBT,to enhance diagnostic precision.

Urea breath test for Helicobacter pylori infection in adult dyspeptic patients: A meta-analysis of diagnostic test accuracy

BACKGROUND Helicobacter pylori(H.pylori)infection has been well-established as a significant risk factor for several gastrointestinal disorders.The urea breath test(UBT)has emerged as a leading non-invasive method for detecting H.pylori.Despite numerous studies confirming its substantial accuracy,the reliability of UBT results is often compromised by inherent limitations.These findings underscore the need for a rigorous statistical synthesis to clarify and reconcile the diagnostic accuracy of the UBT for the diagnosis of H.pylori infection.AIM To determine and compare the diagnostic accuracy of 13C-UBT and 14C-UBT for H.pylori infection in adult patients with dyspepsia.METHODS We conducted an independent search of the PubMed/MEDLINE,EMBASE,and Cochrane Central databases until April 2022.Our search included diagnostic accuracy studies that evaluated at least one of the index tests(^(13)C-UBT or ^(14)C-UBT)against a reference standard.We used the QUADAS-2 tool to assess the methodo-logical quality of the studies.We utilized the bivariate random-effects model to calculate sensitivity,specificity,positive and negative test likelihood ratios(LR+and LR-),as well as the diagnostic odds ratio(DOR),and their 95%confidence intervals.We conducted subgroup analyses based on urea dosing,time after urea administration,and assessment technique.To investigate a possible threshold effect,we conducted Spearman correlation analysis,and we generated summary receiver operating characteristic(SROC)curves to assess heterogeneity.Finally,we visually inspected a funnel plot and used Egger’s test to evaluate publication bias.endorsing both as reliable diagnostic tools in clinical practice.CONCLUSION In summary,our study has demonstrated that ^(13)C-UBT has been found to outperform the ^(14)C-UBT,making it the preferred diagnostic approach.Additionally,our results emphasize the significance of carefully considering urea dosage,assessment timing,and measurement techniques for both tests to enhance diagnostic precision.Nevertheless,it is crucial for researchers and clinicians to evaluate the strengths and limitations of our findings before implementing them in practice.

Infectious Disease Update in Obstetrics: A Modern Approach to the Patient with a Positive Screening Test for Syphyilis in Pregnancy

Screening for maternal syphilis has been an essential component of routine antenatal screening tests in most countries for many years. This is not only because of the virulence of the spirochete which causes the infection but also because of its vertical transmission rate and the potential severe adverse complications/morbidity that can result from its transmission to the fetus. Although the incidence of maternal syphilis and its fetal sequalae in low-income countries has been considerable for several years, the disease has been almost non-existent in high income countries with wide antenatal screening coverage and effective treatment programmes for Syphilis. The recent alarming increase in the incidence of maternal syphilis in high income countries has spawned a renewed public health interest in the infection, with several countries updating and strengthening public health guidance in an attempt to stem this dramatic trend. This is a short clinical update for the practising obstetrician on how to manage the antenatal patient with a positive syphilis screening test.

Efficient Penetration Testing Path Planning Based on Reinforcement Learning with Episodic Memory

Intelligent penetration testing is of great significance for the improvement of the security of information systems,and the critical issue is the planning of penetration test paths.In view of the difficulty for attackers to obtain complete network information in realistic network scenarios,Reinforcement Learning(RL)is a promising solution to discover the optimal penetration path under incomplete information about the target network.Existing RL-based methods are challenged by the sizeable discrete action space,which leads to difficulties in the convergence.Moreover,most methods still rely on experts’knowledge.To address these issues,this paper proposes a penetration path planning method based on reinforcement learning with episodic memory.First,the penetration testing problem is formally described in terms of reinforcement learning.To speed up the training process without specific prior knowledge,the proposed algorithm introduces episodic memory to store experienced advantageous strategies for the first time.Furthermore,the method offers an exploration strategy based on episodic memory to guide the agents in learning.The design makes full use of historical experience to achieve the purpose of reducing blind exploration and improving planning efficiency.Ultimately,comparison experiments are carried out with the existing RL-based methods.The results reveal that the proposed method has better convergence performance.The running time is reduced by more than 20%.