A Novel Predictive Model for Edge Computing Resource Scheduling Based on Deep Neural Network

Currently,applications accessing remote computing resources through cloud data centers is the main mode of operation,but this mode of operation greatly increases communication latency and reduces overall quality of service(QoS)and quality of experience(QoE).Edge computing technology extends cloud service functionality to the edge of the mobile network,closer to the task execution end,and can effectivelymitigate the communication latency problem.However,the massive and heterogeneous nature of servers in edge computing systems brings new challenges to task scheduling and resource management,and the booming development of artificial neural networks provides us withmore powerfulmethods to alleviate this limitation.Therefore,in this paper,we proposed a time series forecasting model incorporating Conv1D,LSTM and GRU for edge computing device resource scheduling,trained and tested the forecasting model using a small self-built dataset,and achieved competitive experimental results.

Predictive Factors for Pre-Eclampsia: A Case-Control Study in Two Hospitals in Yaounde

Introduction: Pre-eclampsia is a major cause of maternal and prenatal morbidity and mortality, that complicates 2% to 8% of pregnancies worldwide. The aim of this study was to determine the predictive factors for pre-eclampsia in two hospitals in the city of Yaoundé. Methods: A case-control study was conducted at the Gynaecology & Obstetrics department of the Yaoundé Gynaeco-Obstetric and Paediatric Hospital (YGOPH) and the Main Maternity of the Yaoundé Central Hospital (MM-YCH) from February 1 to July 30, 2022. The cases were all pregnant women presenting with pre-eclampsia. The control group included pregnant women without pre-eclampsia. Descriptive statistics followed by logistic regression analyses were conducted with level of significance set at p-value Results: Included in the study were 33 cases and 132 controls, giving a total of 165 participants. The predictive factors for pre-eclampsia after multivariate analysis were: primiparity (aOR = 51.86, 95% CI: 3.01 - 1230.96, p = 0.045), duration of exposure to partner’s sperm Conclusion: The odds of pre-eclampsia increased with primiparity, duration of exposure to partner’s sperm < 3 months, personal history of pre-eclampsia and maternal history of pre-eclampsia. Recognition of these predictor factors would improve the ability to diagnose and monitor women likely to develop pre-eclampsia before the onset of disease for timely interventions.

Predictive factors for percutaneous nephrolithotomy bleeding risks

Objective:This study aimed to identify predictive factors for percutaneous nephrolithotomy(PCNL)bleeding risks.With better risk stratification,bleeding in high-risk patient can be anticipated and facilitates early identification.Methods:A prospective observational study of PCNL performed at our institution was done.All adults with radio-opaque renal stones planned for PCNL were included except those with coagulopathy,planned for additional procedures.Factors including gender,co-morbidities,body mass index,stone burden,puncture site,tract dilatation size,operative position,surgeon's seniority,and operative duration were studied using stepwise multivariate regression analysis to identify the predictive factors associated with higher estimated hemoglobin(Hb)deficiency.Results:Overall,4.86%patients(n=7)received packed cells transfusion.The mean estimated Hb deficiency was 1.3(range 0-6.5)g/dL and the median was 1.0 g/dL.Stepwise multivariate regression analysis revealed that absence of hypertension(p=0.024),puncture site(p=0.027),and operative duration(p=0.023)were significantly associated with higher estimated Hb deficiency.However,the effect sizes are rather small with partial eta-squared of 0.037,0.066,and 0.038,respectively.Observed power obtained was 0.621,0.722,and 0.625,respectively.Other factors studied did not correlate with Hb difference.Conclusion:Hypertension,puncture site,and operative duration have significant impact on estimated Hb deficiency during PCNL.However,the effect size is rather small despite adequate study power obtained.Nonetheless,operative position(supine or prone),puncture number,or tract dilatation size did not correlate with Hb difference.The mainstay of reducing bleeding in PCNL is still meticulous operative technique.Our study findings also suggest that PCNL can be safely done by urology trainees under supervision in suitably selected patient,without increasing risk of bleeding.

THAPE: A Tunable Hybrid Associative Predictive Engine Approach for Enhancing Rule Interpretability in Association Rule Learning for the Retail Sector

Association rule learning(ARL)is a widely used technique for discovering relationships within datasets.However,it often generates excessive irrelevant or ambiguous rules.Therefore,post-processing is crucial not only for removing irrelevant or redundant rules but also for uncovering hidden associations that impact other factors.Recently,several post-processing methods have been proposed,each with its own strengths and weaknesses.In this paper,we propose THAPE(Tunable Hybrid Associative Predictive Engine),which combines descriptive and predictive techniques.By leveraging both techniques,our aim is to enhance the quality of analyzing generated rules.This includes removing irrelevant or redundant rules,uncovering interesting and useful rules,exploring hidden association rules that may affect other factors,and providing backtracking ability for a given product.The proposed approach offers a tailored method that suits specific goals for retailers,enabling them to gain a better understanding of customer behavior based on factual transactions in the target market.We applied THAPE to a real dataset as a case study in this paper to demonstrate its effectiveness.Through this application,we successfully mined a concise set of highly interesting and useful association rules.Out of the 11,265 rules generated,we identified 125 rules that are particularly relevant to the business context.These identified rules significantly improve the interpretability and usefulness of association rules for decision-making purposes.

Uncertainty and disturbance estimator-based model predictive control for wet flue gas desulphurization system

Wet flue gas desulphurization technology is widely used in the industrial process for its capability of efficient pollution removal.The desulphurization control system,however,is subjected to complex reaction mechanisms and severe disturbances,which make for it difficult to achieve certain practically relevant control goals including emission and economic performances as well as system robustness.To address these challenges,a new robust control scheme based on uncertainty and disturbance estimator(UDE)and model predictive control(MPC)is proposed in this paper.The UDE is used to estimate and dynamically compensate acting disturbances,whereas MPC is deployed for optimal feedback regulation of the resultant dynamics.By viewing the system nonlinearities and unknown dynamics as disturbances,the proposed control framework allows to locally treat the considered nonlinear plant as a linear one.The obtained simulation results confirm that the utilization of UDE makes the tracking error negligibly small,even in the presence of unmodeled dynamics.In the conducted comparison study,the introduced control scheme outperforms both the standard MPC and PID(proportional-integral-derivative)control strategies in terms of transient performance and robustness.Furthermore,the results reveal that a lowpass-filter time constant has a significant effect on the robustness and the convergence range of the tracking error.

Application of Predictive Model for Efficient Cassava (Manihot esculenta Crantz) Yield in the Face of Climate Variability in Enugu State, Nigeria

Climate variability as occasioned by conditions such as extreme rainfall and temperature, rainfall cessation, and irregular temperatures has considerable impact on crop yield and food security. This study develops a predictive model for cassava yield (Manihot esculenta Crantz) amidst climate variability in rainfed zone of Enugu State, Nigeria. This study utilized data of climate variables and tonnage of cassava yield spanning from 1971 to 2012;as well as information from a questionnaire and focus group discussion from farmers across two seasons in 2023 respectively. Regression analysis was employed to develop the predictive model equation for seasonal climate variability and cassava yield. The rainfall and temperature anomalies, decadal change in trend of cassava yield and opinion of farmers on changes in rainfall season were also computed in the study. The result shows the following relationship between cassava and all the climatic variables: R2 = 0.939;P = 0.00514;Cassava and key climatic variables: R2 = 0.560;P = 0.007. The result implies that seasonal rainfall, temperature, relative humidity, sunshine hours and radiation parameters are key climatic variables in cassava production. This is supported by computed rainfall and temperature anomalies which range from −478.5 to 517.8 mm as well as −1.2˚C to 2.3˚C over the years. The questionnaire and focus group identified that farmers experienced at one time or another, late onset of rain, early onset of rain or rainfall cessation over the years. The farmers are not particularly sure of rainfall and temperature characteristics at any point in time. The implication of the result of this study is that rainfall and temperature parameters determine the farming season and quantity of productivity. Hence, there is urgent need to address the situation through effective and quality weather forecasting network which will help stem food insecurity in the study area and Nigeria at large. The study made recommendations such as a comprehensive early warning system on climate variability incidence which can be communicated to local farmers by agro-meteorological extension officers, research on crops that can grow with little or no rain, planning irrigation scheme, and improving tree planting culture in the study area.

Finite-Time Stabilization for Constrained Discrete-time Systems by Using Model Predictive Control

In this paper, a model predictive control(MPC)framework is proposed for finite-time stabilization of linear and nonlinear discrete-time systems subject to state and control constraints. The proposed MPC framework guarantees the finite-time convergence property by assigning the control horizon equal to the dimension of the overall system, and only penalizing the terminal cost in the optimization, where the stage costs are not penalized explicitly. A terminal inequality constraint is added to guarantee the feasibility and stability of the closed-loop system.Initial feasibility can be improved via augmentation. The finite-time convergence of the proposed MPC is proved theoretically,and is supported by simulation examples.

Game Theory Based Model for Predictive Analytics Using Distributed Position Function

This paper presents a game theory-based method for predicting the outcomes of negotiation and group decision-making problems. We propose an extension to the BDM model to address problems where actors’ positions are distributed over a position spectrum. We generalize the concept of position in the model to incorporate continuous positions for the actors, enabling them to have more flexibility in defining their targets. We explore different possible functions to study the role of the position function and discuss appropriate distance measures for computing the distance between the positions of actors. To validate the proposed extension, we demonstrate the trustworthiness of our model’s performance and interpretation by replicating the results based on data used in earlier studies.

Three predictive scores compared in a retrospective multicenter study of nonunion tibial shaft fracture

BACKGROUND Delayed union,malunion,and nonunion are serious complications in the healing of fractures.Predicting the risk of nonunion before or after surgery is challenging.AIM To compare the most prevalent predictive scores of nonunion used in clinical practice to determine the most accurate score for predicting nonunion.METHODS We collected data from patients with tibial shaft fractures undergoing surgery from January 2016 to December 2020 in three different trauma hospitals.In this retrospective multicenter study,we considered only fractures treated with intramedullary nailing.We calculated the tibia FRACTure prediction healING days(FRACTING)score,Nonunion Risk Determination score,and Leeds-Genoa Nonunion Index(LEG-NUI)score at the time of definitive fixation.RESULTS Of the 130 patients enrolled,89(68.4%)healed within 9 months and were classified as union.The remaining patients(n=41,31.5%)healed after more than 9 months or underwent other surgical procedures and were classified as nonunion.After calculation of the three scores,LEG-NUI and FRACTING were the most accurate at predicting healing.CONCLUSION LEG-NUI and FRACTING showed the best performances by accurately predicting union and nonunion.

Developing and validating a predictive model of delivering large-forgestational-age infants among women with gestational diabetes mellitus

BACKGROUND The birth of large-for-gestational-age(LGA)infants is associated with many shortterm adverse pregnancy outcomes.It has been observed that the proportion of LGA infants born to pregnant women with gestational diabetes mellitus(GDM)is significantly higher than that born to healthy pregnant women.However,traditional methods for the diagnosis of LGA have limitations.Therefore,this study aims to establish a predictive model that can effectively identify women with GDM who are at risk of delivering LGA infants.AIM To develop and validate a nomogram prediction model of delivering LGA infants among pregnant women with GDM,and provide strategies for the effective prevention and timely intervention of LGA.METHODS The multivariable prediction model was developed by carrying out the following steps.First,the variables that were associated with LGA risk in pregnant women with GDM were screened by univariate analyses,for which the P value was<0.10.Subsequently,Least Absolute Shrinkage and Selection Operator regression was fit using ten cross-validations,and the optimal combination factors were se-lected by choosing lambda 1se as the criterion.The final predictors were deter-mined by multiple backward stepwise logistic regression analysis,in which only the independent variables were associated with LGA risk,with a P value<0.05.Finally,a risk prediction model was established and subsequently evaluated by using area under the receiver operating characteristic curve,calibration curve and decision curve analyses.RESULTS After using a multistep screening method,we establish a predictive model.Several risk factors for delivering an LGA infant were identified(P<0.01),including weight gain during pregnancy,parity,triglyceride-glucose index,free tetraiodothyronine level,abdominal circumference,alanine transaminase-aspartate aminotransferase ratio and weight at 24 gestational weeks.The nomogram’s prediction ability was supported by the area under the curve(0.703,0.709,and 0.699 for the training cohort,validation cohort,and test cohort,respectively).The calibration curves of the three cohorts displayed good agreement.The decision curve showed that the use of the 10%-60%threshold for identifying pregnant women with GDM who are at risk of delivering an LGA infant would result in a positive net benefit.CONCLUSION Our nomogram incorporated easily accessible risk factors,facilitating individualized prediction of pregnant women with GDM who are likely to deliver an LGA infant.

Prognostic and predictive role of immune microenvironment in colorectal cancer

Colorectal cancer(CRC)represents a molecularly heterogeneous disease and one of the most frequent causes of cancer-related death worldwide.The traditional classification of CRC is based on pathomorphological and molecular character-istics of tumor cells(mucinous,ring-cell carcinomas,etc.),analysis of mechanisms of carcinogenesis involved(chromosomal instability,microsatellite instability,CpG island methylator phenotype)and mutational statuses of commonly altered genes(KRAS,NRAS,BRAF,APC,etc.),as well as expression signatures(CMS 1-4).It is also suggested that the tumor microenvironment is a key player in tumor progression and metastasis in CRC.According to the latest data,the immune microenvironment can also be predictive of the response to immune checkpoint inhibitors.In this review,we highlight how the immune environment influences CRC prognosis and sensitivity to systemic therapy.

Autonomous Vehicle Platoons In Urban Road Networks:A Joint Distributed Reinforcement Learning and Model Predictive Control Approach

In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework:the deep reinforcement learning output(action) is translated into a set-point to be tracked by the model predictive controller;conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decisionmaking purposes;on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps(links,junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the aim to put in light the main f eatures of the proposed approach. Moreover, it is important to underline that in different operating scenarios the proposed reinforcement learning scheme is capable of significantly reducing traffic congestion phenomena when compared with well-reputed competitors.

Clinical nursing value of predictive nursing in reducing complications of pregnant women undergoing short-term massive blood transfusion during cesarean section

BACKGROUND Cesarean hemorrhage is one of the serious complications,and short-term massive blood transfusion can easily cause postoperative infection and physical stress response.However,predictive nursing intervention has important clinical significance for it.AIM To explore the effect of predictive nursing intervention on the stress response and complications of women undergoing short-term mass blood transfusion during cesarean section(CS).METHODS A clinical medical record of 100 pregnant women undergoing rapid mass blood transfusion during sections from June 2019 to June 2021.According to the different nursing methods,patients divided into control group(n=50)and observation group(n=50).Among them,the control group implemented routine nursing,and the observation group implemented predictive nursing intervention based on the control group.Moreover,compared the differences in stress res-ponse,complications,and pain scores before and after the nursing of pregnant women undergoing rapid mass blood transfusion during CS.RESULTS The anxiety and depression scores of pregnant women in the two groups were significantly improved after nursing,and the psychological stress response of the observation group was significantly lower than that of the control group(P<0.05).The heart rate and mean arterial pressure(MAP)of the observation group during delivery were lower than those of the control group,and the MAP at the end of delivery was lower than that of the control group(P<0.05).Moreover,different pain scores improved significantly in both groups,with the observation group considerably less than the control group(P<0.05).After nursing,complications such as skin rash,urinary retention,chills,diarrhea,and anaphylactic shock in the observation group were 18%,which significantly higher than in the control group(4%)(P<0.05).CONCLUSION Predictive nursing intervention can effectively relieve the pain,reduce the incidence of complications,improve mood and stress response,and serve as a reference value for the nursing of women undergoing rapid mass transfusion during CS.

Predictive value of machine learning models for lymph node metastasis in gastric cancer: A two-center study

BACKGROUND Gastric cancer is one of the most common malignant tumors in the digestive system,ranking sixth in incidence and fourth in mortality worldwide.Since 42.5%of metastatic lymph nodes in gastric cancer belong to nodule type and peripheral type,the application of imaging diagnosis is restricted.AIM To establish models for predicting the risk of lymph node metastasis in gastric cancer patients using machine learning(ML)algorithms and to evaluate their pre-dictive performance in clinical practice.METHODS Data of a total of 369 patients who underwent radical gastrectomy at the Depart-ment of General Surgery of Affiliated Hospital of Xuzhou Medical University(Xuzhou,China)from March 2016 to November 2019 were collected and retro-spectively analyzed as the training group.In addition,data of 123 patients who underwent radical gastrectomy at the Department of General Surgery of Jining First People’s Hospital(Jining,China)were collected and analyzed as the verifi-cation group.Seven ML models,including decision tree,random forest,support vector machine(SVM),gradient boosting machine,naive Bayes,neural network,and logistic regression,were developed to evaluate the occurrence of lymph node metastasis in patients with gastric cancer.The ML models were established fo-llowing ten cross-validation iterations using the training dataset,and subsequently,each model was assessed using the test dataset.The models’performance was evaluated by comparing the area under the receiver operating characteristic curve of each model.RESULTS Among the seven ML models,except for SVM,the other ones exhibited higher accuracy and reliability,and the influences of various risk factors on the models are intuitive.CONCLUSION The ML models developed exhibit strong predictive capabilities for lymph node metastasis in gastric cancer,which can aid in personalized clinical diagnosis and treatment.

Enhancing the vertical resolution of lunar penetrating radar data using predictive deconvolution

The Yutu-2 rover onboard the Chang’E-4 mission performed the first lunar penetrating radar detection on the farside of the Moon.The high-frequency channel presented us with many unprecedented details of the subsurface structures within a depth of approximately 50 m.However,it was still difficult to identify finer layers from the cluttered reflections and scattering waves.We applied deconvolution to improve the vertical resolution of the radar profile by extending the limited bandwidth associated with the emissive radar pulse.To overcome the challenges arising from the mixed-phase wavelets and the problematic amplification of noise,we performed predictive deconvolution to remove the minimum-phase components from the Chang’E-4 dataset,followed by a comprehensive phase rotation to rectify phase anomalies in the radar image.Subsequently,we implemented irreversible migration filtering to mitigate the noise and diminutive clutter echoes amplified by deconvolution.The processed data showed evident enhancement of the vertical resolution with a widened bandwidth in the frequency domain and better signal clarity in the time domain,providing us with more undisputed details of subsurface structures near the Chang’E-4 landing site.

Multi-Time Scale Operation and Simulation Strategy of the Park Based on Model Predictive Control

Due to the impact of source-load prediction power errors and uncertainties,the actual operation of the park will have a wide range of fluctuations compared with the expected state,resulting in its inability to achieve the expected economy.This paper constructs an operating simulation model of the park power grid operation considering demand response and proposes a multi-time scale operating simulation method that combines day-ahead optimization and model predictive control(MPC).In the day-ahead stage,an operating simulation plan that comprehensively considers the user’s side comfort and operating costs is proposed with a long-term time scale of 15 min.In order to cope with power fluctuations of photovoltaic,wind turbine and conventional load,MPC is used to track and roll correct the day-ahead operating simulation plan in the intra-day stage to meet the actual operating operation status of the park.Finally,the validity and economy of the operating simulation strategy are verified through the analysis of arithmetic examples.

Assessing recent recurrence after hepatectomy for hepatitis Brelated hepatocellular carcinoma by a predictive model based on sarcopenia

BACKGROUND Sarcopenia may be associated with hepatocellular carcinoma(HCC)following hepatectomy.But traditional single clinical variables are still insufficient to predict recurrence.We still lack effective prediction models for recent recurrence(time to recurrence<2 years)after hepatectomy for HCC.AIM To establish an interventable prediction model to estimate recurrence-free survival(RFS)after hepatectomy for HCC based on sarcopenia.METHODS We retrospectively analyzed 283 hepatitis B-related HCC patients who underwent curative hepatectomy for the first time,and the skeletal muscle index at the third lumbar spine was measured by preoperative computed tomography.94 of these patients were enrolled for external validation.Cox multivariate analysis was per-formed to identify the risk factors of postoperative recurrence in training cohort.A nomogram model was developed to predict the RFS of HCC patients,and its predictive performance was validated.The predictive efficacy of this model was evaluated using the receiver operating characteristic curve.RESULTS Multivariate analysis showed that sarcopenia[Hazard ratio(HR)=1.767,95%CI:1.166-2.678,P<0.05],alpha-fetoprotein≥40 ng/mL(HR=1.984,95%CI:1.307-3.011,P<0.05),the maximum diameter of tumor>5 cm(HR=2.222,95%CI:1.285-3.842,P<0.05),and hepatitis B virus DNA level≥2000 IU/mL(HR=2.1,95%CI:1.407-3.135,P<0.05)were independent risk factors associated with postoperative recurrence of HCC.Based on the sarcopenia to assess the RFS model of hepatectomy with hepatitis B-related liver cancer disease(SAMD)was established combined with other the above risk factors.The area under the curve of the SAMD model was 0.782(95%CI:0.705-0.858)in the training cohort(sensitivity 81%,specificity 63%)and 0.773(95%CI:0.707-0.838)in the validation cohort.Besides,a SAMD score≥110 was better to distinguish the high-risk group of postoperative recurrence of HCC.CONCLUSION Sarcopenia is associated with recent recurrence after hepatectomy for hepatitis B-related HCC.A nutritional status-based prediction model is first established for postoperative recurrence of hepatitis B-related HCC,which is superior to other models and contributes to prognosis prediction.

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.

Disturbance rejection tube model predictive levitation control of maglev trains

Magnetic levitation control technology plays a significant role in maglev trains.Designing a controller for the levitation system is challenging due to the strong nonlinearity,open-loop instability,and the need for fast response and security.In this paper,we propose a Disturbance-Observe-based Tube Model Predictive Levitation Control(DO-TMPLC)scheme combined with a feedback linearization strategy for the levitation system.The proposed strategy incorporates state constraints and control input constraints,i.e.,the air gap,the vertical velocity,and the current applied to the coil.A feedback linearization strategy is used to cancel the nonlinearity of the tracking error system.Then,a disturbance observer is implemented to actively compensate for disturbances while a TMPLC controller is employed to alleviate the remaining disturbances.Furthermore,we analyze the recursive feasibility and input-to-state stability of the closed-loop system.The simulation results indicate the efficacy of the proposed control strategy.

Establishment of a prognosis predictive model for liver cancer based on expression of genes involved in the ubiquitin-proteasome pathway

BACKGROUND The ubiquitin-proteasome pathway(UPP)has been proven to play important roles in cancer.AIM To investigate the prognostic significance of genes involved in the UPP and develop a predictive model for liver cancer based on the expression of these genes.METHODS In this study,UPP-related E1,E2,E3,deubiquitylating enzyme,and proteasome gene sets were obtained from the Kyoto Encyclopedia of Genes and Genomes(KEGG)database,aiming to screen the prognostic genes using univariate and multivariate regression analysis and develop a prognosis predictive model based RESULTS Five genes(including autophagy related 10,proteasome 20S subunit alpha 8,proteasome 20S subunit beta 2,ubiquitin specific peptidase 17 like family member 2,and ubiquitin specific peptidase 8)were proven significantly correlated with prognosis and used to develop a prognosis predictive model for liver cancer.Among training,validation,and Gene Expression Omnibus sets,the overall survival differed significantly between the high-risk and low-risk groups.The expression of the five genes was significantly associated with immunocyte infiltration,tumor stage,and postoperative recurrence.A total of 111 differentially expressed genes(DEGs)were identified between the high-risk and low-risk groups and they were enriched in 20 and 5 gene ontology and KEGG pathways.Cell division cycle 20,Kelch repeat and BTB domain containing 11,and DDB1 and CUL4 associated factor 4 like 2 were the DEGs in the E3 gene set that correlated with survival.CONCLUSION We have constructed a prognosis predictive model in patients with liver cancer,which contains five genes that associate with immunocyte infiltration,tumor stage,and postoperative recurrence.