Coupled multiphysical model for investigation of influence factors in the application of microbially induced calcite precipitation

The study presents a comprehensive coupled thermo-bio-chemo-hydraulic(T-BCH)modeling framework for stabilizing soils using microbially induced calcite precipitation(MICP).The numerical model considers relevant multiphysics involved in MICP,such as bacterial ureolytic activities,biochemical reactions,multiphase and multicomponent transport,and alteration of the porosity and permeability.The model incorporates multiphysical coupling effects through well-established constitutive relations that connect parameters and variables from different physical fields.It was implemented in the open-source finite element code OpenGeoSys(OGS),and a semi-staggered solution strategy was designed to solve the couplings,allowing for flexible model settings.Therefore,the developed model can be easily adapted to simulate MICP applications in different scenarios.The numerical model was employed to analyze the effect of various factors,including temperature,injection strategies,and application scales.Besides,a TBCH modeling study was conducted on the laboratory-scale domain to analyze the effects of temperature on urease activity and precipitated calcium carbonate.To understand the scale dependency of MICP treatment,a large-scale heterogeneous domain was subjected to variable biochemical injection strategies.The simulations conducted at the field-scale guided the selection of an injection strategy to achieve the desired type and amount of precipitation.Additionally,the study emphasized the potential of numerical models as reliable tools for optimizing future developments in field-scale MICP treatment.The present study demonstrates the potential of this numerical framework for designing and optimizing the MICP applications in laboratory-,prototype-,and field-scale scenarios.

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.

Analysis of risk factors leading to anxiety and depression in patients with prostate cancer after castration and the construction of a risk prediction model

BACKGROUND Cancer patients often suffer from severe stress reactions psychologically,such as anxiety and depression.Prostate cancer(PC)is one of the common cancer types,with most patients diagnosed at advanced stages that cannot be treated by radical surgery and which are accompanied by complications such as bodily pain and bone metastasis.Therefore,attention should be given to the mental health status of PC patients as well as physical adverse events in the course of clinical treatment.AIM To analyze the risk factors leading to anxiety and depression in PC patients after castration and build a risk prediction model.METHODS A retrospective analysis was performed on the data of 120 PC cases treated in Xi'an People's Hospital between January 2019 and January 2022.The patient cohort was divided into a training group(n=84)and a validation group(n=36)at a ratio of 7:3.The patients’anxiety symptoms and depression levels were assessed 2 wk after surgery with the Self-Rating Anxiety Scale(SAS)and the Selfrating Depression Scale(SDS),respectively.Logistic regression was used to analyze the risk factors affecting negative mood,and a risk prediction model was constructed.RESULTS In the training group,35 patients and 37 patients had an SAS score and an SDS score greater than or equal to 50,respectively.Based on the scores,we further subclassified patients into two groups:a bad mood group(n=35)and an emotional stability group(n=49).Multivariate logistic regression analysis showed that marital status,castration scheme,and postoperative Visual Analogue Scale(VAS)score were independent risk factors affecting a patient's bad mood(P<0.05).In the training and validation groups,patients with adverse emotions exhibited significantly higher risk scores than emotionally stable patients(P<0.0001).The area under the curve(AUC)of the risk prediction model for predicting bad mood in the training group was 0.743,the specificity was 70.96%,and the sensitivity was 66.03%,while in the validation group,the AUC,specificity,and sensitivity were 0.755,66.67%,and 76.19%,respectively.The Hosmer-Lemeshow test showed aχ^(2) of 4.2856,a P value of 0.830,and a C-index of 0.773(0.692-0.854).The calibration curve revealed that the predicted curve was basically consistent with the actual curve,and the calibration curve showed that the prediction model had good discrimination and accuracy.Decision curve analysis showed that the model had a high net profit.CONCLUSION In PC patients,marital status,castration scheme,and postoperative pain(VAS)score are important factors affecting postoperative anxiety and depression.The logistic regression model can be used to successfully predict the risk of adverse psychological emotions.

Reduced mesencephalic astrocyte-derived neurotrophic factor expression by mutant androgen receptor contributes to neurodegeneration in a model of spinal and bulbar muscular atrophy pathology

Spinal and bulbar muscular atrophy is a neurodegenerative disease caused by extended CAG trinucleotide repeats in the androgen receptor gene,which encodes a ligand-dependent transcription facto r.The mutant androgen receptor protein,characterized by polyglutamine expansion,is prone to misfolding and forms aggregates in both the nucleus and cytoplasm in the brain in spinal and bulbar muscular atrophy patients.These aggregates alter protein-protein interactions and compromise transcriptional activity.In this study,we reported that in both cultured N2a cells and mouse brain,mutant androgen receptor with polyglutamine expansion causes reduced expression of mesencephalic astrocyte-de rived neurotrophic factor.Overexpressio n of mesencephalic astrocyte-derived neurotrophic factor amelio rated the neurotoxicity of mutant androgen receptor through the inhibition of mutant androgen receptor aggregation.Conversely.knocking down endogenous mesencephalic astrocyte-derived neurotrophic factor in the mouse brain exacerbated neuronal damage and mutant androgen receptor aggregation.Our findings suggest that inhibition of mesencephalic astrocyte-derived neurotrophic factor expression by mutant androgen receptor is a potential mechanism underlying neurodegeneration in spinal and bulbar muscular atrophy.

Modeling Analysis of Factors Influencing Wind-Borne Seed Dispersal: A Case Study on Dandelion

A weed is a plant that thrives in areas of human disturbance, such as gardens, fields, pastures, waysides, and waste places where it is not intentionally cultivated. Dispersal affects community dynamics and vegetation response to global change. The process of seed disposal is influenced by wind, which plays a crucial role in determining the distance and probability of seed dispersal. Existing models of seed dispersal consider wind direction but fail to incorporate wind intensity. In this paper, a novel seed disposal model was proposed in this paper, incorporating wind intensity based on relevant references. According to various climatic conditions, including temperate, arid, and tropical regions, three specific regions were selected to establish a wind dispersal model that accurately reflects the density function distribution of dispersal distance. Additionally, dandelions growth is influenced by a multitude of factors, encompassing temperature, humidity, climate, and various environmental variables that necessitate meticulous consideration. Based on Factor Analysis model, which completely considers temperature, precipitation, solar radiation, wind, and land carrying capacity, a conclusion is presented, indicating that the growth of seeds is primarily influenced by plant attributes and climate conditions, with the former exerting a relatively stronger impact. Subsequently, the remaining two plants were chosen based on seed weight, yielding consistent conclusion.

From Digital Human Modeling to Human Digital Twin: Framework and Perspectives in Human Factors

The human digital twin(HDT)emerges as a promising human-centric technology in Industry 5.0,but challenges remain in human modeling and simulation.Digital human modeling(DHM)provides solutions for modeling and simulating human physical and cognitive aspects to support ergonomic analysis.However,it has limitations in real-time data usage,personalized services,and timely interaction.The emerging HDT concept offers new possibilities by integrating multi-source data and artificial intelligence for continuous monitoring and assessment.Hence,this paper reviews the evolution from DHM to HDT and proposes a unified HDT framework from a human factors perspective.The framework comprises the physical twin,the virtual twin,and the linkage between these two.The virtual twin integrates human modeling and AI engines to enable model-data-hybrid-enabled simulation.HDT can potentially upgrade traditional ergonomic methods to intelligent services through real-time analysis,timely feedback,and bidirectional interactions.Finally,the future perspectives of HDT for industrial applications as well as technical and social challenges are discussed.In general,this study outlines a human factors perspective on HDT for the first time,which is useful for cross-disciplinary research and human factors innovation to enhance the development of HDT in industry.

A Prognostic Model Based on Colony Stimulating Factors-related Genes in Triple-negative Breast Cancer

Objective Triple-negative breast cancer(TNBC)is the breast cancer subtype with the worst prognosis,and lacks effective therapeutic targets.Colony stimulating factors(CSFs)are cytokines that can regulate the production of blood cells and stimulate the growth and development of immune cells,playing an important role in the malignant progression of TNBC.This article aims to construct a novel prognostic model based on the expression of colony stimulating factors-related genes(CRGs),and analyze the sensitivity of TNBC patients to immunotherapy and drug therapy.Methods We downloaded CRGs from public databases and screened for differentially expressed CRGs between normal and TNBC tissues in the TCGA-BRCA database.Through LASSO Cox regression analysis,we constructed a prognostic model and stratified TNBC patients into high-risk and low-risk groups based on the colony stimulating factors-related genes risk score(CRRS).We further analyzed the correlation between CRRS and patient prognosis,clinical features,tumor microenvironment(TME)in both high-risk and low-risk groups,and evaluated the relationship between CRRS and sensitivity to immunotherapy and drug therapy.Results We identified 842 differentially expressed CRGs in breast cancer tissues of TNBC patients and selected 13 CRGs for constructing the prognostic model.Kaplan-Meier survival curves,time-dependent receiver operating characteristic curves,and other analyses confirmed that TNBC patients with high CRRS had shorter overall survival,and the predictive ability of CRRS prognostic model was further validated using the GEO dataset.Nomogram combining clinical features confirmed that CRRS was an independent factor for the prognosis of TNBC patients.Moreover,patients in the high-risk group had lower levels of immune infiltration in the TME and were sensitive to chemotherapeutic drugs such as 5-fluorouracil,ipatasertib,and paclitaxel.Conclusion We have developed a CRRS-based prognostic model composed of 13 differentially expressed CRGs,which may serve as a useful tool for predicting the prognosis of TNBC patients and guiding clinical treatment.Moreover,the key genes within this model may represent potential molecular targets for future therapies of TNBC.

Influence of perinatal factors on full-term low-birth-weight infants and construction of a predictive model

BACKGROUND Being too light at birth can increase the risk of various diseases during infancy.AIM To explore the effect of perinatal factors on term low-birth-weight(LBW)infants and build a predictive model.This model aims to guide the clinical management of pregnant women’s healthcare during pregnancy and support the healthy growth of newborns.METHODS A retrospective analysis was conducted on data from 1794 single full-term pregnant women who gave birth.Newborns were grouped based on birth weight:Those with birth weight<2.5 kg were classified as the low-weight group,and those with birth weight between 2.5 kg and 4 kg were included in the normal group.Multiple logistic regression analysis was used to identify the factors influencing the occurrence of full-term LBW.A risk prediction model was established based on the analysis results.The effectiveness of the model was analyzed using the Hosmer–Leme show test and receiver operating characteristic(ROC)curve to verify the accuracy of the predictions.RESULTS Among the 1794 pregnant women,there were 62 cases of neonatal weight<2.5 kg,resulting in an LBW incidence rate of 3.46%.The factors influencing full-term LBW included low maternal education level[odds ratio(OR)=1.416],fewer prenatal examinations(OR=2.907),insufficient weight gain during pregnancy(OR=3.695),irregular calcium supplementation during pregnancy(OR=1.756),and pregnancy hypertension syndrome(OR=2.192).The prediction model equation was obtained as follows:Logit(P)=0.348×maternal education level+1.067×number of prenatal examinations+1.307×insufficient weight gain during pregnancy+0.563×irregular calcium supplementation during pregnancy+0.785×pregnancy hypertension syndrome−29.164.The area under the ROC curve for this model was 0.853,with a sensitivity of 0.852 and a specificity of 0.821.The Hosmer–Leme show test yieldedχ^(2)=2.185,P=0.449,indicating a good fit.The overall accuracy of the clinical validation model was 81.67%.CONCLUSION The occurrence of full-term LBW is related to maternal education,the number of prenatal examinations,weight gain during pregnancy,calcium supplementation during pregnancy,and pregnancy-induced hypertension.The constructed predictive model can effectively predict the risk of full-term LBW.

Analysis of risk factors of suicidal ideation in adolescent patients with depression and construction of prediction model

BACKGROUND Major depressive disorder is a common mental illness among adolescents and is the largest disease burden in this age group.Most adolescent patients with depression have suicidal ideation(SI);however,few studies have focused on the factors related to SI,and effective predictive models are lacking.AIM To construct a risk prediction model for SI in adolescent depression and provide a reference assessment tool for prevention.METHODS The data of 150 adolescent patients with depression at the First People's Hospital of Lianyungang from June 2020 to December 2022 were retrospectively analyzed.Based on whether or not they had SI,they were divided into a SI group(n=91)and a non-SI group(n=59).The general data and laboratory indices of the two groups were compared.Logistic regression was used to analyze the factors influencing SI in adolescent patients with depression,a nomogram prediction model was constructed based on the analysis results,and internal evaluation was performed.Receiver operating characteristic and calibration curves were used to evaluate the model’s efficacy,and the clinical application value was evaluated using decision curve analysis(DCA).RESULTS There were differences in trauma history,triggers,serum ferritin levels(SF),highsensitivity C-reactive protein levels(hs-CRP),and high-density lipoprotein(HDLC)levels between the two groups(P<0.05).Logistic regression analysis showed that trauma history,predisposing factors,SF,hs-CRP,and HDL-C were factors influencing SI in adolescent patients with depression.The area under the curve of the nomogram prediction model was 0.831(95%CI:0.763–0.899),sensitivity was 0.912,and specificity was 0.678.The higher net benefit of the DCA and the average absolute error of the calibration curve were 0.043,indicating that the model had a good fit.CONCLUSION The nomogram prediction model based on trauma history,triggers,ferritin,serum hs-CRP,and HDL-C levels can effectively predict the risk of SI in adolescent patients with depression.

Identification of risk factors and construction of a nomogram predictive model for post-stroke infection in patients with acute ischemic stroke

BACKGROUND Post-stroke infection is the most common complication of stroke and poses a huge threat to patients.In addition to prolonging the hospitalization time and increasing the medical burden,post-stroke infection also significantly increases the risk of disease and death.Clarifying the risk factors for post-stroke infection in patients with acute ischemic stroke(AIS)is of great significance.It can guide clinical practice to perform corresponding prevention and control work early,minimizing the risk of stroke-related infections and ensuring favorable disease outcomes.AIM To explore the risk factors for post-stroke infection in patients with AIS and to construct a nomogram predictive model.METHODS The clinical data of 206 patients with AIS admitted to our hospital between April 2020 and April 2023 were retrospectively collected.Baseline data and post-stroke infection status of all study subjects were assessed,and the risk factors for poststroke infection in patients with AIS were analyzed.RESULTS Totally,48 patients with AIS developed stroke,with an infection rate of 23.3%.Age,diabetes,disturbance of consciousness,high National Institutes of Health Stroke Scale(NIHSS)score at admission,invasive operation,and chronic obstructive pulmonary disease(COPD)were risk factors for post-stroke infection in patients with AIS(P<0.05).A nomogram prediction model was constructed with a C-index of 0.891,reflecting the good potential clinical efficacy of the nomogram prediction model.The calibration curve also showed good consistency between the actual observations and nomogram predictions.The area under the receiver operating characteristic curve was 0.891(95%confidence interval:0.839–0.942),showing predictive value for post-stroke infection.When the optimal cutoff value was selected,the sensitivity and specificity were 87.5%and 79.7%,respectively.CONCLUSION Age,diabetes,disturbance of consciousness,NIHSS score at admission,invasive surgery,and COPD are risk factors for post-stroke infection following AIS.The nomogram prediction model established based on these factors exhibits high discrimination and accuracy.

Enrichment model and major controlling factors of below-source tight oil in Lower Cretaceous Fuyu reservoirs in northern Songliao Basin,NE China

Based on the geochemical,seismic,logging and drilling data,the Fuyu reservoirs of the Lower Cretaceous Quantou Formation in northern Songliao Basin are systematically studied in terms of the geological characteristics,the tight oil enrichment model and its major controlling factors.First,the Quantou Formation is overlaid by high-quality source rocks of the Upper Cretaceous Qingshankou Formation,with the development of nose structure around sag and the broad and continuous distribution of sand bodies.The reservoirs are tight on the whole.Second,the configuration of multiple elements,such as high-quality source rocks,reservoir rocks,fault,overpressure and structure,controls the tight oil enrichment in the Fuyu reservoirs.The source-reservoir combination controls the tight oil distribution pattern.The pressure difference between source and reservoir drives the charging of tight oil.The fault-sandbody transport system determines the migration and accumulation of oil and gas.The positive structure is the favorable place for tight oil enrichment,and the fault-horst zone is the key part of syncline area for tight oil exploration.Third,based on the source-reservoir relationship,transport mode,accumulation dynamics and other elements,three tight oil enrichment models are recognized in the Fuyu reservoirs:(1)vertical or lateral migration of hydrocarbon from source rocks to adjacent reservoir rocks,that is,driven by overpressure,hydrocarbon generated is migrated vertically or laterally to and accumulates in the adjacent reservoir rocks;(2)transport of hydrocarbon through faults between separated source and reservoirs,that is,driven by overpressure,hydrocarbon migrates downward through faults to the sandbodies that are separated from the source rocks;and(3)migration of hydrocarbon through faults and sandbodies between separated source and reservoirs,that is,driven by overpressure,hydrocarbon migrates downwards through faults to the reservoir rocks that are separated from the source rocks,and then migrates laterally through sandbodies.Fourth,the differences in oil source conditions,charging drive,fault distribution,sandbody and reservoir physical properties cause the differential enrichment of tight oil in the Fuyu reservoirs.Comprehensive analysis suggests that the Fuyu reservoir in the Qijia-Gulong Sag has good conditions for tight oil enrichment and has been less explored,and it is an important new zone for tight oil exploration in the future.

Risk factors and survival prediction model establishment for prognosis in patients with radical resection of gallbladder cancer

BACKGROUND Gallbladder cancer(GBC)is the most common malignant tumor of the biliary system,and is often undetected until advanced stages,making curative surgery unfeasible for many patients.Curative surgery remains the only option for long-term survival.Accurate postsurgical prognosis is crucial for effective treatment planning.tumor-node-metastasis staging,which focuses on tumor infiltration,lymph node metastasis,and distant metastasis,limits the accuracy of prognosis.Nomograms offer a more comprehensive and personalized approach by visually analyzing a broader range of prognostic factors,enhancing the precision of treatment planning for patients with GBC.AIM A retrospective study analyzed the clinical and pathological data of 93 patients who underwent radical surgery for GBC at Peking University People's Hospital from January 2015 to December 2020.Kaplan-Meier analysis was used to calculate the 1-,2-and 3-year survival rates.The log-rank test was used to evaluate factors impacting prognosis,with survival curves plotted for significant variables.Single-factor analysis revealed statistically significant differences,and multivariate Cox regression identified independent prognostic factors.A nomogram was developed and validated with receiver operating characteristic curves and calibration curves.Among 93 patients who underwent radical surgery for GBC,30 patients survived,accounting for 32.26%of the sample,with a median survival time of 38 months.The 1-year,2-year,and 3-year survival rates were 83.87%,68.82%,and 53.57%,respectively.Univariate analysis revealed that carbohydrate antigen 19-9 expre-ssion,T stage,lymph node metastasis,histological differentiation,surgical margins,and invasion of the liver,ex-trahepatic bile duct,nerves,and vessels(P≤0.001)significantly impacted patient prognosis after curative surgery.Multivariate Cox regression identified lymph node metastasis(P=0.03),histological differentiation(P<0.05),nerve invasion(P=0.036),and extrahepatic bile duct invasion(P=0.014)as independent risk factors.A nomogram model with a concordance index of 0.838 was developed.Internal validation confirmed the model's consistency in predicting the 1-year,2-year,and 3-year survival rates.CONCLUSION Lymph node metastasis,tumor differentiation,extrahepatic bile duct invasion,and perineural invasion are independent risk factors.A nomogram based on these factors can be used to personalize and improve treatment strategies.

Comparative Analysis of the Factors Influencing Metro Passenger Arrival Volumes in Wuhan, China, and Lagos, Nigeria: An Application of Association Rule Mining and Neural Network Models

This study explores the factors influencing metro passengers’ arrival volume in Wuhan, China, and Lagos, Nigeria, by examining weather, time of day, waiting time, travel behavior, arrival patterns, and metro satisfaction. It addresses a significant research gap in understanding metro passengers’ dynamics across cultural and geographical contexts. It employs questionnaires, field observations, and advanced data analysis techniques like association rule mining and neural network modeling. Key findings include a correlation between rainy weather, shorter waiting times, and higher arrival volumes. Neural network models showed high predictive accuracy, with waiting time, metro satisfaction, and weather being significant factors in Lagos Light Rail Blue Line Metro. In contrast, arrival patterns, weather, and time of day were more influential in Wuhan Metro Line 5. Results suggest that improving metro satisfaction and reducing waiting times could increase arrival volumes in Lagos Metro while adjusting schedules for weather and peak times could optimize flow in Wuhan Metro. These insights are valuable for transportation planning, passenger arrival volume management, and enhancing user experiences, potentially benefiting urban transportation sustainability and development goals.

A Study on the Factors Influencing Consumer Purchase Decision Under the Live-Streaming Sales Model

In recent years,with the rapid development and popularization of Internet information technology,many new media platforms have risen rapidly,and major e-commerce companies have begun to explore the mode of livestreaming.Especially during the COVID-19 pandemic,due to the lockdown,live-streaming has become an important means of economic development in many places.Owing to its remarkable characteristics of timeliness,entertainment,and interactivity,it has become the latest and trendiest sales mode of e-commerce channels,reflecting huge economic potential and commercial value.This article analyzes two models and their characteristics of live-streaming sales from a practical perspective.Based on this,it outlines consumer purchasing decisions and the factors that affect consumer purchasing decisions under the live-streaming sales model.Finally,it discusses targeted suggestions for using the live-streaming sales model to expand the consumer market,hoping to promote the healthy and steady development of the live-streaming sales industry.

R-Factor Analysis of Data Based on Population Models Comprising R- and Q-Factors Leads to Biased Loading Estimates

Effects of performing an R-factor analysis of observed variables based on population models comprising R- and Q-factors were investigated. Although R-factor analysis of data based on a population model comprising R- and Q-factors is possible, this may lead to model error. Accordingly, loading estimates resulting from R-factor analysis of sample data drawn from a population based on a combination of R- and Q-factors will be biased. It was shown in a simulation study that a large amount of Q-factor variance induces an increase in the variation of R-factor loading estimates beyond the chance level. Tests of the multivariate kurtosis of observed variables are proposed as an indicator of possible Q-factor variance in observed variables as a prerequisite for R-factor analysis.

Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors

To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides.The research results have important reference significance for land use and landslide prevention.

Controlling factors and models of shale oil enrichment in Lower Permian Fengcheng Formation,Mahu Sag,Junggar Basin,NW China

Based on the combination of core observation,experimental analysis and testingand geological analysis,the main controlling factors of shale oil enrichment in the Lower Permian Fengcheng Formation in the Mahu Sag of the Junggar Basin are clarified,and a shale oil enrichment model is established.The results show that the enrichment of shale oil in the Fengcheng Formation in the Mahu Sag is controlled by the organic abundance,organic type,reservoir capacity and the amount of migration hydrocarbon in shale.The abundance of organic matter provides the material basis for shale oil enrichment,and the shales containing typesⅠandⅡorganic matters have good oil content.The reservoir capacity controls shale oil enrichment.Macropores are the main space for shale oil enrichment in the Fengcheng Formation,and pore size and fracture scale directly control the degree of shale oil enrichment.The migration of hydrocarbons in shale affects shale oil enrichment.The shale that has expelled hydrocarbons has poor oil content,while the shale that has received hydrocarbons migrated from other strata has good oil content.Lithofacies reflect the hydrocarbon generation and storage capacity comprehensively.The laminated felsic shale,laminated lime-dolomitic shale and thick-layered felsic shale have good oil content,and they are favorable lithofacies for shale oil enrichment.Under the control of these factors,relative migration of hydrocarbons occurred within the Fengcheng shale,which leads to the the difference in the enrichment process of shale oil.Accordingly,the enrichment mode of shale oil in Fengcheng Formation is established as"in-situ enrichment"and"migration enrichment".By superimposing favorable lithofacies and main controlling factors of enrichment,the sweet spot of shale oil in the Fengcheng Formation can be selected which has great significance for the exploration and development of shale oil.

Risk factors and prediction model for inpatient surgical site infection after elective abdominal surgery

BACKGROUND Surgical site infections(SSIs) are the commonest healthcare-associated infection. In addition to increasing mortality, it also lengthens the hospital stay and raises healthcare expenses. SSIs are challenging to predict, with most models having poor predictability. Therefore, we developed a prediction model for SSI after elective abdominal surgery by identifying risk factors.AIM To analyse the data on inpatients undergoing elective abdominal surgery to identify risk factors and develop predictive models that will help clinicians assess patients preoperatively.METHODS We retrospectively analysed the inpatient records of Shaanxi Provincial People’s Hospital from January 1, 2018 to January 1, 2021. We included the demographic data of the patients and their haematological test results in our analysis. The attending physicians provided the Nutritional Risk Screening 2002(NRS 2002)scores. The surgeons and anaesthesiologists manually calculated the National Nosocomial Infections Surveillance(NNIS) scores. Inpatient SSI risk factors were evaluated using univariate analysis and multivariate logistic regression. Nomograms were used in the predictive models. The receiver operating characteristic and area under the curve values were used to measure the specificity and accuracy of the model.RESULTS A total of 3018 patients met the inclusion criteria. The surgical sites included the uterus(42.2%), the liver(27.6%), the gastrointestinal tract(19.1%), the appendix(5.9%), the kidney(3.7%), and the groin area(1.4%). SSI occurred in 5% of the patients(n = 150). The risk factors associated with SSI were as follows: Age;gender;marital status;place of residence;history of diabetes;surgical season;surgical site;NRS 2002 score;preoperative white blood cell, procalcitonin(PCT), albumin, and low-density lipoprotein cholesterol(LDL) levels;preoperative antibiotic use;anaesthesia method;incision grade;NNIS score;intraoperative blood loss;intraoperative drainage tube placement;surgical operation items. Multivariate logistic regression revealed the following independent risk factors: A history of diabetes [odds ratio(OR) = 5.698, 95% confidence interval(CI): 3.305-9.825, P = 0.001], antibiotic use(OR = 14.977, 95%CI: 2.865-78.299, P = 0.001), an NRS 2002 score of ≥ 3(OR = 2.426, 95%CI: 1.199-4.909, P = 0.014), general anaesthesia(OR = 3.334, 95%CI: 1.134-9.806, P = 0.029), an NNIS score of ≥ 2(OR = 2.362, 95%CI: 1.019-5.476, P = 0.045), PCT ≥ 0.05 μg/L(OR = 1.687, 95%CI: 1.056-2.695, P = 0.029), LDL < 3.37 mmol/L(OR = 1.719, 95%CI: 1.039-2.842, P = 0.035), intraoperative blood loss ≥ 200 mL(OR = 29.026, 95%CI: 13.751-61.266, P < 0.001), surgical season(P < 0.05), surgical site(P < 0.05), and incision grade I or Ⅲ(P < 0.05). The overall area under the receiver operating characteristic curve of the predictive model was 0.926, which is significantly higher than the NNIS score(0.662).CONCLUSION The patient’s condition and haematological test indicators form the bases of our prediction model. It is a novel, efficient, and highly accurate predictive model for preventing postoperative SSI, thereby improving the prognosis in patients undergoing abdominal surgery.

基于24Model-D-ISM的地铁站火灾疏散影响因素研究

为预防地铁站火灾事故,深入了解地铁站火灾人员疏散影响因素间的内在联系与层次结构,基于第6版“2-4”模型(24Model)分析63起地铁站火灾疏散事故,充分考虑各个因素之间的交互作用,提取19个影响地铁站人员疏散的关键因素,建立地铁站火灾人员疏散影响因素指标体系;采用算子客观赋权法(C-OWA)改进决策试验与评价实验法(DEMATEL),确定地铁站火灾人员疏散的重要影响因素;在此基础上,采用解释结构模型(ISM)分析各个因素间的层次结构及相互作用路径,构建地铁站火灾人员疏散影响因素的多级递阶结构模型。研究结果表明:疏散引导、恐慌从众行为、人员拥挤为地铁站火灾人员疏散的关键影响因素;地铁站火灾人员疏散受表层因素、中间层因素、深层因素共同作用的影响,其中,疏散教育与培训、设施维护与检查、疏散预案等因素是根源影响因素,重视根源影响因素的改善有利于从本质上预防和控制事故的发生。

Analysis of influencing factors and the construction of predictive models for postpartum depression in older pregnant women

BACKGROUND Changes in China's fertility policy have led to a significant increase in older pregnant women.At present,there is a lack of analysis of influencing factors and research on predictive models for postpartum depression(PPD)in older pregnant women.AIM To analysis the influencing factors and the construction of predictive models for PPD in older pregnant women.METHODS By adopting a cross-sectional survey research design,239 older pregnant women(≥35 years old)who underwent obstetric examinations and gave birth at Suzhou Ninth People's Hospital from February 2022 to July 2023 were selected as the research subjects.When postpartum women of advanced maternal age came to the hospital for follow-up 42 d after birth,the Edinburgh PPD Scale(EPDS)was used to assess the presence of PPD symptoms.The women were divided into a PPD group and a no-PPD group.Two sets of data were collected for analysis,and a prediction model was constructed.The performance of the predictive model was evaluated using receiver operating characteristic(ROC)analysis and the Hosmer-Lemeshow goodness-of-fit test.RESULTS On the 42nd day after delivery,51 of 239 older pregnant women were evaluated with the EPDS scale and found to have depressive symptoms.The incidence rate was 21.34%(51/239).There were statistically significant differences between the PPD group and the no-PPD group in terms of education level(P=0.004),family relationships(P=0.001),pregnancy complications(P=0.019),and mother–infant separation after birth(P=0.002).Multivariate logistic regression analysis showed that a high school education and below,poor family relationships,pregnancy complications,and the separation of the mother and baby after birth were influencing factors for PPD in older pregnant women(P<0.05).Based on the influencing factors,the following model equation was developed:Logit(P)=0.729×education level+0.942×family relationship+1.137×pregnancy complications+1.285×separation of the mother and infant after birth-6.671.The area under the ROC curve of this prediction model was 0.873(95%CI:0.821-0.924),the sensitivity was 0.871,and the specificity was 0.815.The deviation between the value predicted by the model and the actual value through the Hosmer-Lemeshow goodness-of-fit test was not statistically significant(χ^(2)=2.749,P=0.638),indicating that the model did not show an overfitting phenomenon.CONCLUSION The risk of PPD among older pregnant women is influenced by educational level,family relationships,pregnancy complications,and the separation of the mother and baby after birth.A prediction model based on these factors can effectively predict the risk of PPD in older pregnant women.