Enhancing Software Effort Estimation:A Hybrid Model Combining LSTM and Random Forest

Effort estimation plays a crucial role in software development projects,aiding in resource allocation,project planning,and risk management.Traditional estimation techniques often struggle to provide accurate estimates due to the complex nature of software projects.In recent years,machine learning approaches have shown promise in improving the accuracy of effort estimation models.This study proposes a hybrid model that combines Long Short-Term Memory(LSTM)and Random Forest(RF)algorithms to enhance software effort estimation.The proposed hybrid model takes advantage of the strengths of both LSTM and RF algorithms.To evaluate the performance of the hybrid model,an extensive set of software development projects is used as the experimental dataset.The experimental results demonstrate that the proposed hybrid model outperforms traditional estimation techniques in terms of accuracy and reliability.The integration of LSTM and RF enables the model to efficiently capture temporal dependencies and non-linear interactions in the software development data.The hybrid model enhances estimation accuracy,enabling project managers and stakeholders to make more precise predictions of effort needed for upcoming software projects.

Nursing model of midwifery and postural and psychological interventions:Impact on maternal and fetal outcomes and negative emotions of primiparas

BACKGROUND Primiparas are usually at high risk of experiencing perinatal depression,which may cause prolonged labor,increased blood loss,and intensified pain,affecting maternal and fetal outcomes.Therefore,interventions are necessary to improve maternal and fetal outcomes and alleviate primiparas’negative emotions(NEs).AIM To discusses the impact of nursing responsibility in midwifery and postural and psychological interventions on maternal and fetal outcomes as well as primiparas’NEs.METHODS As participants,115 primiparas admitted to Quanzhou Maternity and Child Healthcare Hospital between May 2020 and May 2022 were selected.Among them,56 primiparas(control group,Con)were subjected to conventional midwifery and routine nursing.The remaining 59(research group,Res)were subjected to the nursing model of midwifery and postural and psychological interventions.Both groups were comparatively analyzed from the perspectives of delivery mode(cesarean,natural,or forceps-assisted),maternal and fetal outcomes(uterine inertia,postpartum hemorrhage,placental abruption,neonatal pulmonary injury,and neonatal asphyxia),NEs(Hamilton Anxiety/Depressionrating Scale,HAMA/HAMD),labor duration,and nursing satisfaction.RESULTS The Res exhibited a markedly higher natural delivery rate and nursing satisfaction than the Con.Additionally,the Res indicated a lower incidence of adverse events(e.g.,uterine inertia,postpartum hemorrhage,placental abruption,neonatal lung injury,and neonatal asphyxia)and shortened duration of various stages of labor.It also showed statistically lower post-interventional HAMA and HAMD scores than the Con and pre-interventional values.CONCLUSION The nursing model of midwifery and postural and psychological interventions increase the natural delivery rate and reduce the duration of each labor stage.These are also conducive to improving maternal and fetal outcomes and mitigating primiparas’NEs and thus deserve popularity in clinical practice.

Effect of Internet+continuous midwifery service model on psychological mood and pregnancy outcomes for women with highrisk pregnancies

BACKGROUND There are many drawbacks to the traditional midwifery service management model,which can no longer meet the needs of the new era.The Internet+continuous midwifery service management model extends maternal management from prenatal to postpartum,in-hospital to out-of-hospital,and offline to online,thereby improving maternal and infant outcomes.Applying the Internet+continuous midwifery service management model to manage women with highrisk pregnancies(HRP)can improve their psycho-emotional opinion and,in turn,minimize the risk of adverse maternal and/or fetal outcomes.AIM To explore the effectiveness of a midwife-led Internet+continuous midwifery service model for women with HRP.METHODS We retrospectively analyzed the clinical data of 439 women with HRP who underwent prenatal examination and delivered at Shanghai Sixth People's Hospital(affiliated to the Shanghai Jiao Tong University School of Medicine)from April to December 2022.Among them,239 pregnant women underwent routine obstetric management,and 200 pregnant women underwent Internet+continuous midwifery service mode management.We used the State-Trait Anxiety Inventory,Edinburgh Postnatal Depression Scale,and analysis of delivery outcomes to compare psychological mood and the incidence of adverse delivery outcomes between the two groups.RESULTS The data showed that in early pregnancy,the anxiety and depression levels of the two groups were similar;the levels gradually decreased as pregnancy progressed,and the decrease in the continuous group was more significant[31.00(29.00,34.00)vs 34.00(32.00,37.00),8.00(6.00,9.00)vs 12.00(10.00,13.00),P<0.05].The maternal self-efficacy level and strategy for weight gain management were better in the continuous group than in the traditional group,and the effective rate of midwifery service intervention in the continuous group was significantly higher than in the control group[267.50(242.25,284.75)vs 256.00(233.00,278.00),74.00(69.00,78.00)vs 71.00(63.00,78.00),P<0.05].The incidence of adverse delivery outcomes in pregnant women and newborns and fear of maternal childbirth were lower in the continuous group than in the traditional group,and nursing satisfaction was higher[10.50%vs 18.83%,8.50%vs 15.90%,24.00%vs 42.68%,89.50%vs 76.15%,P<0.05].CONCLUSION The Internet+continuous midwifery service model promotes innovation through integration and is of great significance for improving and promoting maternal and child health in HRP.

The pig as a preclinical traumatic brain injury model:current models,functional outcome measures,and translational detection strategies

Traumatic brain injury(TBI) is a major contributor of long-term disability and a leading cause of death worldwide. A series of secondary injury cascades can contribute to cell death, tissue loss, and ultimately to the development of functional impairments. However, there are currently no effective therapeutic interventions that improve brain outcomes following TBI. As a result, a number of experimental TBI models have been developed to recapitulate TBI injury mechanisms and to test the efficacy of potential therapeutics. The pig model has recently come to the forefront as the pig brain is closer in size, structure, and composition to the human brain compared to traditional rodent models, making it an ideal large animal model to study TBI pathophysiology and functional outcomes. This review will focus on the shared characteristics between humans and pigs that make them ideal for modeling TBI and will review the three most common pig TBI models–the diffuse axonal injury, the controlled cortical impact, and the fluid percussion models. It will also review current advances in functional outcome assessment measures and other non-invasive, translational TBI detection and measurement tools like biomarker analysis and magnetic resonance imaging. The use of pigs as TBI models and the continued development and improvement of translational assessment modalities have made significant contributions to unraveling the complex cascade of TBI sequela and provide an important means to study potential clinically relevant therapeutic interventions.

Predictive modeling for postoperative delirium in elderly patients with abdominal malignancies using synthetic minority oversampling technique

BACKGROUND Postoperative delirium,particularly prevalent in elderly patients after abdominal cancer surgery,presents significant challenges in clinical management.AIM To develop a synthetic minority oversampling technique(SMOTE)-based model for predicting postoperative delirium in elderly abdominal cancer patients.METHODS In this retrospective cohort study,we analyzed data from 611 elderly patients who underwent abdominal malignant tumor surgery at our hospital between September 2020 and October 2022.The incidence of postoperative delirium was recorded for 7 d post-surgery.Patients were divided into delirium and non-delirium groups based on the occurrence of postoperative delirium or not.A multivariate logistic regression model was used to identify risk factors and develop a predictive model for postoperative delirium.The SMOTE technique was applied to enhance the model by oversampling the delirium cases.The model’s predictive accuracy was then validated.RESULTS In our study involving 611 elderly patients with abdominal malignant tumors,multivariate logistic regression analysis identified significant risk factors for postoperative delirium.These included the Charlson comorbidity index,American Society of Anesthesiologists classification,history of cerebrovascular disease,surgical duration,perioperative blood transfusion,and postoperative pain score.The incidence rate of postoperative delirium in our study was 22.91%.The original predictive model(P1)exhibited an area under the receiver operating characteristic curve of 0.862.In comparison,the SMOTE-based logistic early warning model(P2),which utilized the SMOTE oversampling algorithm,showed a slightly lower but comparable area under the curve of 0.856,suggesting no significant difference in performance between the two predictive approaches.CONCLUSION This study confirms that the SMOTE-enhanced predictive model for postoperative delirium in elderly abdominal tumor patients shows performance equivalent to that of traditional methods,effectively addressing data imbalance.

Prediction of Outcomes in Mini-Basketball Training Program for Preschool Children with Autism Using Machine Learning Models

In recent years evidence has emerged suggesting that Mini-basketball training program(MBTP)can be an effec-tive intervention method to improve social communication(SC)impairments and restricted and repetitive beha-viors(RRBs)in preschool children suffering from autism spectrum disorder(ASD).However,there is a considerable degree if interindividual variability concerning these social outcomes and thus not all preschool chil-dren with ASD profit from a MBTP intervention to the same extent.In order to make more accurate predictions which preschool children with ASD can benefit from an MBTP intervention or which preschool children with ASD need additional interventions to achieve behavioral improvements,further research is required.This study aimed to investigate which individual factors of preschool children with ASD can predict MBTP intervention out-comes concerning SC impairments and RRBs.Then,test the performance of machine learning models in predict-ing intervention outcomes based on these factors.Participants were 26 preschool children with ASD who enrolled in a quasi-experiment and received MBTP intervention.Baseline demographic variables(e.g.,age,body,mass index[BMI]),indicators of physicalfitness(e.g.,handgrip strength,balance performance),performance in execu-tive function,severity of ASD symptoms,level of SC impairments,and severity of RRBs were obtained to predict treatment outcomes after MBTP intervention.Machine learning models were established based on support vector machine algorithm were implemented.For comparison,we also employed multiple linear regression models in statistics.Ourfindings suggest that in preschool children with ASD symptomatic severity(r=0.712,p<0.001)and baseline SC impairments(r=0.713,p<0.001)are predictors for intervention outcomes of SC impair-ments.Furthermore,BMI(r=-0.430,p=0.028),symptomatic severity(r=0.656,p<0.001),baseline SC impair-ments(r=0.504,p=0.009)and baseline RRBs(r=0.647,p<0.001)can predict intervention outcomes of RRBs.Statistical models predicted 59.6%of variance in post-treatment SC impairments(MSE=0.455,RMSE=0.675,R2=0.596)and 58.9%of variance in post-treatment RRBs(MSE=0.464,RMSE=0.681,R2=0.589).Machine learning models predicted 83%of variance in post-treatment SC impairments(MSE=0.188,RMSE=0.434,R2=0.83)and 85.9%of variance in post-treatment RRBs(MSE=0.051,RMSE=0.226,R2=0.859),which were better than statistical models.Ourfindings suggest that baseline characteristics such as symptomatic severity of 144 IJMHP,2022,vol.24,no.2 ASD symptoms and SC impairments are important predictors determining MBTP intervention-induced improvements concerning SC impairments and RBBs.Furthermore,the current study revealed that machine learning models can successfully be applied to predict the MBTP intervention-related outcomes in preschool chil-dren with ASD,and performed better than statistical models.Ourfindings can help to inform which preschool children with ASD are most likely to benefit from an MBTP intervention,and they might provide a reference for the development of personalized intervention programs for preschool children with ASD.

Chinese nursing postgraduates'views on implementing outcome-oriented academic English course:a descriptive study

Background:According to previous studies on professional English course teaching,the evaluation of course teaching was positive,but the vast majorities focus on medical English literature reading,professional English vocabulary,and professional English translation.As an alternative,the course design based on academic learning needs under the outcome-oriented education/model emphasizes the improvement of students'comprehensive ability in oral expression,literature reading,writing,and academic communication.Objectives:The objective of this study was to analyze nursing postgraduates'opinions on learning the outcome-oriented academic English course.Methods:This is a cross-sectional descriptive study.A total of 150 first-year nursing postgraduates enrolled in the“Academic Professional English for Nursing Postgraduates”course.After completing the course learning,students scanned QR codes generated by the online questionnaire and completed it anonymously within 48 h.Results:The students who participated in this course strongly believed that it“helped them strengthen their English speakability”(4.8 points),“made them more confident to participate in international academic conferences and exchanges in the future”(4.8 points),and“helped them apply English more in the nursing professional field in the future”(4.7 points).Conclusions:The implementation of outcome-oriented course teaching helps students to understand the research of foreign scholars and effectively express their own research content with English as a tool.It motivates them to continuously use English for professional and academic communication.

Effect analysis of applying high-quality service model to surgical nursing

BACKGROUND Surgical care of the hand plays a crucial role in the medical field,as problems with the hand can profoundly affect a patient's quality of life and function.In order to meet the needs of patients,improve patient satisfaction and improve treatment outcomes,high-quality service models have been introduced in the field of nursing.AIM To explore the effect analysis of applying high-quality service model to surgical nursing.METHODS We conducted a retrospective study of patients who underwent hand surgery at our hospital between 2019 and 2022,using a quality service model that included improved patient education,pain management,care team collaboration,and effective communication.Another group of patients received traditional care as a control group.We compared postoperative recovery,satisfaction,complication rate,and length of hospital stay between the two groups.Inferential statistics were used to compare the difference between the two groups by independent sample t test,Chi-square test and other methods to evaluate the effect of intervention measures.RESULTS Postoperative recovery time decreased from 17.8±2.3 d to 14.5±2.1 d,pain score decreased from 4.7±1.9 to 3.2±1.4,and hand function score increased from 78.4±7.1 to 88.5±6.2.In terms of patient satisfaction,the quality service model group scored 87.3±5.6 points,which was significantly higher than that of the traditional care group(74.6±6.3 points).At the same time,patients'understanding of medical information also improved from 6.9±1.4 to 8.6±1.2.In terms of postoperative complications,the application of the quality service model reduced the incidence of postoperative complications from 26%to 10%,the incidence of infection from 12%to 5%,and the incidence of bleeding from 10%to 3%.The reduction in these data indicates that the quality service model plays a positive role in reducing the risk of complications.In addition,the average hospital stay of patients in the quality service model group was shortened from 6.8±1.5 d to 5.2±1.3 d,and the hospitalization cost was also reduced from 2800±600 yuan to 2500±500 yuan.CONCLUSION Applying a quality service model to hand surgery care can significantly improve patient clinical outcomes,including faster recovery,less pain,greater satisfaction,and reduced complication rates.

Multi-outcome predictive modelling of anesthesia patients

Conjunctive use of anesthetic agents results in drug interactions which can alter or influence multiple patient outcomes such as anesthesia depth,and cardiorespiratory parameters which can also be altered by patient conditions and surgical procedures.Using artificial intelligence technology to continuously gather data of drug infusion and patient outcomes,we can generate reliable computer models individualized for a patient during specific stages of particular surgical procedures.This data can then be used to extend the current anesthesia monitoring functions to include future impact prediction,drug administration planning,and anesthesia decisions.

Adaptive Random Effects/Coefficients Modeling

Adaptive fractional polynomial modeling of general correlated outcomes is formulated to address nonlinearity in means, variances/dispersions, and correlations. Means and variances/dispersions are modeled using generalized linear models in fixed effects/coefficients. Correlations are modeled using random effects/coefficients. Nonlinearity is addressed using power transforms of primary (untransformed) predictors. Parameter estimation is based on extended linear mixed modeling generalizing both generalized estimating equations and linear mixed modeling. Models are evaluated using likelihood cross-validation (LCV) scores and are generated adaptively using a heuristic search controlled by LCV scores. Cases covered include linear, Poisson, logistic, exponential, and discrete regression of correlated continuous, count/rate, dichotomous, positive continuous, and discrete numeric outcomes treated as normally, Poisson, Bernoulli, exponentially, and discrete numerically distributed, respectively. Example analyses are also generated for these five cases to compare adaptive random effects/coefficients modeling of correlated outcomes to previously developed adaptive modeling based on directly specified covariance structures. Adaptive random effects/coefficients modeling substantially outperforms direct covariance modeling in the linear, exponential, and discrete regression example analyses. It generates equivalent results in the logistic regression example analyses and it is substantially outperformed in the Poisson regression case. Random effects/coefficients modeling of correlated outcomes can provide substantial improvements in model selection compared to directly specified covariance modeling. However, directly specified covariance modeling can generate competitive or substantially better results in some cases while usually requiring less computation time.

Modelling Influenza Vaccination Outcomes

Modelling response to influenza vaccination can improve our understanding of how proposed factors, older age, past exposure to influenza viruses, and health disorders, used together, affect antibody production after influenza vaccination. Knowledge about this may be important when planning influenza vaccination protocols. This problem will be emphasized especially in the future, when many alternative vaccines and vaccination approaches are likely to be allowed for a routine use. A major difficulty, in modelling response to influenza vaccination, is how to identify health parameters, suitable for general use. To deal with the complexity of this task, we reached out for the concept of a systems biology and machine learning methods. Based on this approach, we showed that it is possible to construct useful models of influenza vaccination outcomes. In addition, by varying criteria for definition of the model’s outcome measure, that is, of low antibody response to influenza vaccination, we showed that a set of health parameters, albeit limited, are necessary for model to achieve a wider practical use.

A Simulation Study on Comparing General Class of Semiparametric Transformation Models for Survival Outcome with Time-Varying Coefficients and Covariates

The consideration of the time-varying covariate and time-varying coefficient effect in survival models are plausible and robust techniques. Such kind of analysis can be carried out with a general class of semiparametric transformation models. The aim of this article is to develop modified estimating equations under semiparametric transformation models of survival time with time-varying coefficient effect and time-varying continuous covariates. For this, it is important to organize the data in a counting process style and transform the time with standard transformation classes which shall be applied in this article. In the situation when the effect of coefficient and covariates change over time, the widely used maximum likelihood estimation method becomes more complex and burdensome in estimating consistent estimates. To overcome this problem, alternatively, the modified estimating equations were applied to estimate the unknown parameters and unspecified monotone transformation functions. The estimating equations were modified to incorporate the time-varying effect in both coefficient and covariates. The performance of the proposed methods is tested through a simulation study. To sum up the study, the effect of possibly time-varying covariates and time-varying coefficients was evaluated in some special cases of semiparametric transformation models. Finally, the results have shown that the role of the time-varying covariate in the semiparametric transformation models was plausible and credible.

Assessing the performance of different outcomes for tumor growth studies with animal models

The consistency of reporting results for patient-derived xenograft(PDX) studies is an area of concern. The PDX method commonly starts by implanting a derivative of a human tumor into a mouse, then comparing the tumor growth under different treatment conditions. Currently, a wide array of statistical methods(e.g., t-test, regression, chi-squared test) are used to analyze these data, which ultimately depend on the outcome chosen(e.g., tumor volume, relative growth, categorical growth). In this simulation study, we provide empirical evidence for the outcome selection process by comparing the performance of both commonly used outcomes and novel variations of common outcomes used in PDX studies. Data were simulated to mimic tumor growth under multiple scenarios, then each outcome of interest was evaluated for 10?000 iterations. Comparisons between different outcomes were made with respect to average bias, variance, type-1 error, and power. A total of 18 continuous, categorical, and time-to-event outcomes were evaluated, with ultimately 2 outcomes outperforming the others: final tumor volume and change in tumor volume from baseline.Notably, the novel variations of the tumor growth inhibition index(TGII)— a commonly used outcome in PDX studies— was found to perform poorly in several scenarios with inflated type-1 error rates and a relatively large bias. Finally, all outcomes of interest were applied to a real-world dataset.

Using the Technology Acceptance Model to Analyze the Learning Outcome of Open Education Resources

Along with the development of information and communications technology,open educational resources were widely applied in training usage.The use of these resources facilitates the access to knowledge by enabling learners to transcend time and space.In this way,learners are able to obtain new knowledge more actively and efficiently than before.Using Technology Acceptance Model(TAM)as the theoretical foundation,this study aims to explore the learning outcome of using open educational resources with the perceived convenience as the external variable.In this study,the open educational resources were defined as online courses on the Open Course Ware(OCW)and Massive Open Online Courses(MOOCs),on which the learners choose courses themselves and study without the impact from people,matters,time,space,and things with the help of the Internet.To achieve the objectives of the study,the researchers conducted a survey with the participants who had already used the open educational resources.In total,124 valid samples were collected.The Partial Least Squares(PLS)statistical method was used to carry out the analysis.Overall,the model of this study has good prediction and explanatory power.After the data analysis,the study found that the perceived convenience exerts a positive impact on the use of the open educational resources.In addition,among the four TAM variables,the perceived usefulness does not exert a significant impact on the behavioral intention to use,but the other three TAM variables all have a significant impact on the behavioral intention.

Development and Validation of a New Prognostic Model for Predicting Survival Outcomes in Patients with Acute-onchronic Liver Failure

Background and Aims:Early determination of prognosis in patients with acute-on-chronic liver failure(ACLF)is crucial for optimizing treatment options and liver allocation.This study aimed to identify risk factors associated with ACLF and to develop new prognostic models that accurately predict patient outcomes.Methods:We retrospectively selected 1,952 hospitalized patients diagnosed with ACLF between January 2010 and June 2018.This cohort was used to develop new prognostic scores,which were subsequently validated in external groups.Results:The study included 1,386 ACLF patients and identified six independent predictors of 28-day mortality through multivariate analysis(all p<0.05).The new score,based on a multivariate regression model,demonstrated superior predictive accuracy for both 28-day and 90-day mortalities,with Areas under the ROC curves of 0.863 and 0.853,respectively(all p<0.05).This score can be used to stratify the risk of mortality among ACLF patients with ACLF,showing a significant difference in survival between patients categorized by the cut-off value(log-rank(Mantel-Cox)χ^(2)=487.574 and 606.441,p=0.000).Additionally,the new model exhibited good robustness in two external cohorts.Conclusions:This study presents a refined prognostic model,the Model for end-stage liver disease-complication score,which accurately predicts short-term mortality in ACLF patients.This model offers a new perspective and tool for improved clinical decision-making and short-term prognostic assessment in ACLF patients.

Outcomes of patients with cirrhosis undergoing non-hepatic surgery:Risk assessment and management

The reported mortality rates in patients with cirrhosis undergoing various non-transplant surgical procedures range from 8.3% to 25%. This wide range of mortality rates is related to severity of liver disease, type of surgery, demographics of patient population, expertise of the surgical, anesthesia and intensive care unit team and finally, reporting bias. In this article, we will review the pathophysiology, morbidity and mortality associated with non-hepatic surgery in patients with cirrhosis, and then recommend an algorithm for risk assessment and evidence based management strategy to optimize post-surgical outcomes.

Association of fluid balance trajectories with clinical outcomes in patients with septic shock:A prospective multicenter cohort study

Background:Septic shock has a high incidence and mortality rate in Intensive Care Units(ICUs).Earlier intravenous fluid resuscitation can significantly improve outcomes in septic patients but easily leads to fluid overload(FO),which is associated with poor clinical outcomes.A single point value of fluid cannot provide enough fluid information.The aim of this study was to investigate the impact of fluid balance(FB)latent trajectories on clinical outcomes in septic patients.Methods:Patients were diagnosed with septic shock during the first 48 h,and sequential fluid data for the first 3 days of ICU admission were included.A group-based trajectory model(GBTM)which is designed to identify groups of individuals following similar developmental trajectories was used to identify latent subgroups of individuals following a similar progression of FB.The primary outcomes were hospital mortality,organ dysfunction,major adverse kidney events(MAKE)and severe respiratory adverse events(SRAE).We used multivariable Cox or logistic regression analysis to assess the association between FB trajectories and clinical outcomes.Results:Nine hundred eighty-six patients met the inclusion criteria and were assigned to GBTM analysis,and three latent FB trajectories were detected.64(6.5%),841(85.3%),and 81(8.2%)patients were identified to have decreased,low,and high FB,respectively.Compared with low FB,high FB was associated with increased hospital mortality[hazard ratio(HR)=1.63,95%CI 1.22–2.17],organ dysfunction[odds ratio(OR)=2.18,95%CI 1.22–3.42],MAKE(OR=1.80,95%CI 1.04–2.63)and SRAE(OR=2.33,95%CI 1.46–3.71),and decreasing FB was significantly associated with decreased MAKE(OR=0.46,95%CI 0.29–0.79)after adjustment for potential covariates.Conclusion:Latent subgroups of septic patients followed a similar FB progression.These latent fluid trajectories were associated with clinical outcomes.The decreasing FB trajectory was associated with a decreased risk of hospital mortality and MAKE.

Incorporating S-shaped testing-effort functions into NHPP software reliability model with imperfect debugging

Testing-effort（TE） and imperfect debugging（ID） in the reliability modeling process may further improve the fitting and prediction results of software reliability growth models（SRGMs）. For describing the S-shaped varying trend of TE increasing rate more accurately, first, two S-shaped testing-effort functions（TEFs）, i.e.,delayed S-shaped TEF（DS-TEF） and inflected S-shaped TEF（IS-TEF）, are proposed. Then these two TEFs are incorporated into various types（exponential-type, delayed S-shaped and inflected S-shaped） of non-homogeneous Poisson process（NHPP）SRGMs with two forms of ID respectively for obtaining a series of new NHPP SRGMs which consider S-shaped TEFs as well as ID. Finally these new SRGMs and several comparison NHPP SRGMs are applied into four real failure data-sets respectively for investigating the fitting and prediction power of these new SRGMs.The experimental results show that：（i） the proposed IS-TEF is more suitable and flexible for describing the consumption of TE than the previous TEFs;（ii） incorporating TEFs into the inflected S-shaped NHPP SRGM may be more effective and appropriate compared with the exponential-type and the delayed S-shaped NHPP SRGMs;（iii） the inflected S-shaped NHPP SRGM considering both IS-TEF and ID yields the most accurate fitting and prediction results than the other comparison NHPP SRGMs.

Development and validation of a predictive model for the assessment of potassium-lowering treatment among hyperkalemia patients

BACKGROUND:Hyperkalemia is common among patients in emergency department and is associated with mortality.While,there is a lack of good evaluation and prediction methods for the effi cacy of potassium-lowering treatment,making the drug dosage adjustment quite diffi cult.We aimed to develop a predictive model to provide early forecasting of treating eff ects for hyperkalemia patients.METHODS:Around 80%of hyperkalemia patients(n=818)were randomly selected as the training dataset and the remaining 20%(n=196)as the validating dataset.According to the serum potassium(K+)levels after the fi rst round of potassium-lowering treatment,patients were classifi ed into the eff ective and ineff ective groups.Multivariate logistic regression analyses were performed to develop a prediction model.The receiver operating characteristic(ROC)curve and calibration curve analysis were used for model validation.RESULTS:In the training dataset,429 patients had favorable eff ects after treatment(eff ective group),and 389 had poor therapeutic outcomes(ineff ective group).Patients in the ineff ective group had a higher percentage of renal disease(P=0.007),peripheral edema(P<0.001),oliguria(P=0.001),or higher initial serum K+level(P<0.001).The percentage of insulin usage was higher in the effective group than in the ineff ective group(P=0.005).After multivariate logistic regression analysis,we found age,peripheral edema,oliguria,history of kidney transplantation,end-stage renal disease,insulin,and initial serum K+were all independently associated with favorable treatment eff ects.CONCLUSION:The predictive model could provide early forecasting of therapeutic outcomes for hyperkalemia patients after drug treatment,which could help clinicians to identify hyperkalemia patients with high risk and adjust the dosage of medication for potassium-lowering.

Model simulations to determine optimal fishing effort and season duration for a tropical shrimp fishery

Kuwait's shrimp fishery presents typical tropical shrimp fishery characteristics with highly variable recruitment, fast growth and strong seasonal patterns. Both the General Production Model and Age-structured Model were chosen to assess the stock status of the Kuwait's shrimp fishery. The estimated Maximum Sustainable Yield(MSY) was 2 518 metric ton(t) with a corresponding fishing eff orts( f MSY) 7 265 standard boat-days from the General Production Model. Similar results from the Age-structured Model were 1 936 t and 6 449 boat-days respectively. Comparing these results with the average annual shrimp landings(1 772 t) and average fishing eff ort(9 710 boat-days) in the past 10 years, we concluded that the fishery was overfished. Model simulations to show the changes of recruitment, biomass and possible catch under different fishing eff ort scenarios indicated possible stock collapse if the fishing eff ort continually increase. But both shrimp recruitment and biomass will increase if the current fishing eff ort is reduced. Model simulations also showed a possible increase of MSY by delaying the opening or by closing the season earlier. Based on these results, recommendations to improve the management of Kuwait's shrimp fishery are presented.