Risk factors and risk prediction model for mucocutaneous separation in enterostomy patients:A single center experience

BACKGROUND Mucocutaneous separation(MCS)is a common postoperative complication in enterostomy patients,potentially leading to significant morbidity.Early identification of risk factors is crucial for preventing this condition.However,predictive models for MCS remain underdeveloped.AIM To construct a risk prediction model for MCS in enterostomy patients and assess its clinical predictive accuracy.METHODS A total of 492 patients who underwent enterostomy from January 2019 to March 2023 were included in the study.Patients were divided into two groups,the MCS group(n=110),and the non-MCS(n=382)based on the occurrence of MCS within the first 3 weeks after surgery.Univariate and multivariate analyses were used to identify the independent predictive factors of MCS and the model constructed.Receiver operating characteristic curve analysis was used to assess the model’s performance.RESULTS The postoperative MCS incidence rate was 22.4%.Suture dislodgement(P<0.0001),serum albumin level(P<0.0001),body mass index(BMI)(P=0.0006),hemoglobin level(P=0.0409),intestinal rapture(P=0.0043),incision infection(P<0.0001),neoadjuvant therapy(P=0.0432),stoma site(P=0.0028)and elevated intra-abdominal pressure(P=0.0395)were potential predictive factors of MCS.Suture dislodgement[P<0.0001,OR:28.007595%CI:(11.0901-82.1751)],serum albumin level(P=0.0008,OR:0.3504,95%CI:[0.1902-0.6485]),BMI[P=0.0045,OR:2.1361,95%CI:(1.2660-3.6235)],hemoglobin level[P=0.0269,OR:0.5164,95%CI:(0.2881-0.9324)],intestinal rapture[P=0.0351,OR:3.0694,95%CI:(1.0482-8.5558)],incision infection[P=0.0179,OR:0.2885,95%CI:(0.0950-0.7624)]and neoadjuvant therapy[P=0.0112,OR:1.9769,95%CI:(1.1718-3.3690)]were independent predictive factors and included in the model.The model had an area under the curve of 0.827 and good clinical utility on decision curve analysis.CONCLUSION The mucocutaneous separation prediction model constructed in this study has good predictive performance and can provide a reference for early warning of mucocutaneous separation in enterostomy patients.

Particulate flow modelling in a spiral separator by using the Eulerian multi-fluid VOF approach

The Euler-Euler model is less effective in capturing the free surface of flow film in the spiral separator,and thus a Eulerian multi-fluid volume of fluid(VOF)model was first proposed to describe the particulate flow in spiral separators.In order to improve the applicability of the model in the high solid concentration system,the Bagnold effect was incorporated into the modelling framework.The capability of the proposed model in terms of predicting the flow film shape in a LD9 spiral separator was evaluated via comparison with measured flow film thicknesses reported in literature.Results showed that sharp air–water and air-pulp interfaces can be obtained using the proposed model,and the shapes of the predicted flow films before and after particle addition were reasonably consistent with the observations reported in literature.Furthermore,the experimental and numerical simulation of the separation of quartz and hematite were performed in a laboratory-scale spiral separator.When the Bagnold lift force model was considered,predictions of the grade of iron and solid concentration by mass for different trough lengths were more consistent with experimental data.In the initial development stage,the quartz particles at the bottom of the flow layer were more possible to be lifted due to the Bagnold force.Thus,a better predicted vertical stratification between quartz and hematite particles was obtained,which provided favorable conditions for subsequent radial segregation.

Numerical modeling of destress blasting for strata separation

Destress blasting(DB)implemented along the perimeter of safety pillars is a special application of destressing in coal longwall mining.The goal is to separate relatively more deformed mined areas from safety pillars,such as shaft pillars or cross-cut pillars,to reduce the transfer of high stresses to the protective pillar.This case study aims to numerically simulate selected destress blasts in the Czech part of the Upper Silesian Coal Basin and examine its impact on stress transfer to the safety pillar area.To separate the area between the protective pillar and the longwall(LW),two fans of five 93-mm blast holes(length of 93e100 m)were drilled from the gate roads into the overburden strata.Each set of blast holes was fired separately in two stages without time delay.The explosive charge(gelatin-type of explosive)of each stage is 3450 kg.The two DB stages were fired when the longwall face was approximately 158 m and 152 m away from the blast.A 3D mine-wide model is built and validated with in situ stress measured with hydrofracturing.Mining and destressing in three 5-m thick coal seams are simulated in the region.Numerical modeling of DB is successfully conducted using a rock fragmentation factor a of 0.05 and a stress reduction/dissipation factor β of 0.95.Buffering of transfer of additional stress from the mining area into the safety pillar is evaluated by comparison of yielding volume before and after DB.It is shown that yielding volume drops after DB by nearly 80%in the area of the destressing panel and near the safety shaft pillar.

A unified Minorization-Maximization approach for estimation of general mixture models

The mixed distribution model is often used to extract information from heteroge-neous data and perform modeling analysis.When the density function of mixed distribution is complicated or the variable dimension is high,it usually brings challenges to the parameter es-timation of the mixed distribution model.The application of MM algorithm can avoid complex expectation calculations,and can also solve the problem of high-dimensional optimization by decomposing the objective function.In this paper,MM algorithm is applied to the parameter estimation problem of mixed distribution model.The method of assembly and decomposition is used to construct the substitute function with separable parameters,which avoids the problems of complex expectation calculations and the inversion of high-dimensional matrices.

Virtually coupled train set control subject to space-time separation:A distributed economic MPC approach with emergency braking configuration

The emerging virtual coupling technology aims to operate multiple train units in a Virtually Coupled Train Set(VCTS)at a minimal but safe distance.To guarantee collision avoidance,the safety distance should be calculated using the state-of-the-art space-time separation principle that separates the Emergency Braking(EB)trajectories of two successive units during the whole EB process.In this case,the minimal safety distance is usually numerically calculated without an analytic formulation.Thus,the constrained VCTS control problem is hard to address with space-time separation,which is still a gap in the existing literature.To solve this problem,we propose a Distributed Economic Model Predictive Control(DEMPC)approach with computation efficiency and theoretical guarantee.Specifically,to alleviate the computation burden,we transform implicit safety constraints into explicitly linear ones,such that the optimal control problem in DEMPC is a quadratic programming problem that can be solved efficiently.For theoretical analysis,sufficient conditions are derived to guarantee the recursive feasibility and stability of DEMPC,employing compatibility constraints,tube techniques and terminal ingredient tuning.Moreover,we extend our approach with globally optimal and distributed online EB configuration methods to shorten the minimal distance among VCTS.Finally,experimental results demonstrate the performance and advantages of the proposed approaches.

Loose-stratification model in separation process for vanadium pre-concentration from stone coal

A technology of one-stage roughing and one-stage scavenging vanadium pre-concentration with shaking table was investigated for improving vanadium grade and decreasing acid consumption minerals content based on the quantitative evaluation of minerals by scanning electronic microscopy （QEMSCAN）. In order to visually illustrate how the vanadium-bearing minerals were separated from system, a loose-stratification model was established with Bagnold shear loose theory and Kelly stratification hypothesis. Through the model, it was inferred that fine fraction and coarse fraction of vanadium-bearing muscovite particles easily became the concentrate in roughing and scavenging stages, respectively. The type of the dominant effect on the loose-stratification was confirmed. In the roughing stage, gravity sedimentation played a leading role in the loose-stratification process. However, in the scavenging stage, shearing dispersion pressure caused by asymmetric motion of table deck took an important part in the loose-stratification process. Finally, the correction of the loose-stratification model was validated by the practical experiment.

Variable projection algorithms with sparse constraint for separable nonlinear models

Separable nonlinear models are widely used in various fields such as time series analysis, system modeling, and machine learning, due to their flexible structures and ability to capture nonlinear behavior of data. However, identifying the parameters of these models is challenging, especially when sparse models with better interpretability are desired by practitioners. Previous theoretical and practical studies have shown that variable projection (VP) is an efficient method for identifying separable nonlinear models, but these are based on \(L_2\) penalty of model parameters, which cannot be directly extended to deal with sparse constraint. Based on the exploration of the structural characteristics of separable models, this paper proposes gradient-based and trust-region-based variable projection algorithms, which mainly solve two key problems: how to eliminate linear parameters under sparse constraint;and how to deal with the coupling relationship between linear and nonlinear parameters in the model. Finally, numerical experiments on synthetic data and real time series data are conducted to verify the effectiveness of the proposed algorithms.

A velocity-difference-separation model for car-following theory

We introduce a velocity-difference-separation model that modifies the previous models in the literature. The improvement of this new model over the previous ones lies in that it not only theoretically retains many strong points of the previous ones, but also performs more realistically than others in the dynamical evolution of congestion. Furthermore, the proposed model is investigated with analytic and numerical methods, with the finding that it can demonstrate some complex physical features observed in real traffic such as the existence of three phases： free flow, synchronized flow, and wide moving jam; sudden flow drop in flow-density plane; and traffic hysteresis in transition between the free and the synchronized flow.

A Variable Separation Approach to Solve the Integrable and Nonintegrable Models:Coherent Structures of the (2 + 1)-Dimensional KdV Eqnation

We study the localized coherent structures ofa generally nonintegrable (2+ 1 )-dimensional KdV equation via a variable separation approach. In a special integrable case, the entrance of some arbitrary functions leads to abundant coherent structures. However, in the general nonintegrable case, an additional condition has to be introduced for these arbitrary functions. Although the additional condition has been introduced into the solutions of the nonintegrable KdV equation, there still exist many interesting solitary wave structures. Especially, the nonintegrable KdV equation possesses the breather-like localized excitations, and the similar static ring soliton solutions as in the integrable case. Furthermor,in the integrable case, the interaction between two travelling ring solitons is elastic, while in the nonintegrable case we cannot find even the single travelling ring soliton solution.

Development of separation sharpness model for hydrocyclone

Hydrocyclones are mechanical devices used in classifying and separating many different types of materials.A classification function of the hydrocyclone has been continually developed for solid–liquid separation.In the classification process of solids from liquids,it is desirable to reduce the amount of misplaced material;therefore,the separation sharpness,α(alpha),is a parameter that helps in evaluating misplaced material and has been developed as a model to help the designer predict the performance of the classification.However,the problem with the separation sharpness model is that it cannot be used outside the range of conditions under which it was developed.Therefore,this research aimed to develop the separation sharpness model to predict more accurately and cover a wide range of conditions using the multiple linear regression method.The new regression model of separation sharpness was based on a wide range of both experimental and industrial data-sets of 431 tests collaborating with the additional experiments of 117 tests that were obtained from a total of 548 tests.The new model of separation sharpness can be used in the range of 30–762 mm hydrocyclone body diameters and feed solid concentrations in the range of 0.5 wt%–80 wt%.When compared with the experimental separation sharpness,the accuracy of the separation sharpness model prediction has an error of 4.53%and^of 0.973.

Testing the robustness of particle-based separation models for the magnetic separation of a complex skarn ore

Physical separation processes are best understood in terms of the behaviour of individual ore particles.Yet,while different empirical particle-based separation modelling approaches have been developed,their predictive performance has never been tested under variable process conditions.Here,we investigated the predictive performance of a state-of-the-art particle-based separation model under variable feed composition for a laboratory-scale magnetic separation of a skarn ore.Two scenarios were investigated:one in which the mass flow of the different processing streams could be measured and one in which it had to be estimated from data.In both scenarios,the predictive models were sufficiently general to predict the process outcomes of new samples of variable composition.Nevertheless,the scenario in which mass flow could be measured was4%more precise in predicting mass balances.The process behaviour of minerals present at concentrations above 0.1%by weight could be accurately predicted.Our findings indicate the potential use of this method to minimize the costs of metallurgical testwork while providing in-depth understanding of the recovery behaviour of individual ore particles.Moreover,the method may be used to establish powerful tools to forecast mineral recoveries for partly new ore types at a running mining operation.

A continuous and high-efficiency process to separate coal bed methane with porous ZIF-8 slurry:Experimental study and mathematical modelling

Coal bed methane has been considered as an important energy resource.One major difficulty of purifying coal bed methane comes from the similar physical properties of CH_4 and N_2.The ZIF-8/water-glycol slurry was used as a medium to separate coal bed methane by fluidifying the solid adsorbent material.The sorption equilibrium experiment of binary mixture(CH_4/N_2)and slurry was conducted.The selectivity of CH_4 to N_2 is within the range of 2-6,which proved the feasibility of the slurry separation method.The modified Langmuir equation was used to describe the gas-slurry phase equilibrium behavior,and the calculated results were in good agreement with the experimental data.A continuous absorption-adsorption and desorption process on the separation of CH_4/N_2 in slurry is proposed and its mathematical model is also developed.Sensitivity analysis is conducted to determine the operation conditions and the energy performance of the proposed process was also evaluated.Feed gas contains 30 mol%of methane and the methane concentration in product gas is 95.46 mol%with the methane recovery ratio of 90.74%.The total energy consumption for per unit volume of product gas is determined as 1.846 kWh Nm^(-3).Experimental results and process simulation provide basic data for the design and operation of pilot and industrial plant.

Optimization of CO_2 separation technologies for Chinese refineries based on a fuzzy comprehensive evaluation model

This study aims at determining the optimal CO2 separation technology for Chinese refineries, based on current available technologies, by the method of comprehensive evaluation. Firstly, according to the characteristics of flue gas from Chinese refineries, three feasible CO2 separation technologies are selected. These are pressure swing adsorption （PSA）, chemical absorption （CA）, and membrane absorption （MA）. Secondly, an economic assessment of these three techniques is carried out in accordance with cash flow analysis. The results show that these three techniques all have economic feasibility and the PSA technique is the best. Finally, to further optimize the three techniques, a two-level fuzzy comprehensive evaluation model is established, including economic, technological, and environmental factors. Considering all the factors, PSA is optimal for Chinese refineries, followed by CA and MA. Therefore, to reduce Chinese refineries carbon emission, it is suggested that CO2 should be captured from off-gases using PSA.

Blind source separation of ship-radiated noise based on generalized Gaussian model

When the distribution of the sources cannot be estimated accurately, the ICA algorithms failed to separate the mixtures blindly. The generalized Gaussian model （GGM） is presented in ICA algorithm since it can model non- Ganssian statistical structure of different source signals easily. By inferring only one parameter, a wide class of statistical distributions can be characterized. By using maximum likelihood （ML） approach and natural gradient descent, the learning rules of blind source separation （BSS） based on GGM are presented. The experiment of the ship-radiated noise demonstrates that the GGM can model the distributions of the ship-radiated noise and sea noise efficiently, and the learning rules based on GGM gives more successful separation results after comparing it with several conventional methods such as high order cumnlants and Gaussian mixture density function.

Evaluation of a mathematical model using experimental data and artificial neural network for prediction of gas separation

In recent times, membranes have found wide applications in gas separation processes. As most of the industrial membrane separation units use hollow fiber modules, having a proper model for simulating this type of membrane module is very useful in achieving guidelines for design and characterization of membrane separation units. In this study, a model based on Coker, Freeman, and Fleming＇s study was used for estimating the required membrane area. This model could simulate a multicomponent gas mixture separation by solving the governing differential mass balance equations with numerical methods. Results of the model were validated using some binary and multicomponent experimental data from the literature. Also, the artificial neural network （ANN） technique was applied to predict membrane gas separation behavior and the results of the ANN simulation were compared with the simulation results of the model and the experimental data. Good consistency between these results shows that ANN method can be successfully used for prediction of the separation behavior after suitable training of the network

Maneuvering target state estimation based on separate model-ing of target trajectory shape and dynamic characteristics

The state estimation of a maneuvering target,of which the trajectory shape is independent on dynamic characteristics,is studied.The conventional motion models in Cartesian coordinates imply that the trajectory of a target is completely determined by its dynamic characteristics.However,this is not true in the applications of road-target,sea-route-target or flight route-target tracking,where target trajectory shape is uncoupled with target velocity properties.In this paper,a new estimation algorithm based on separate modeling of target trajectory shape and dynamic characteristics is proposed.The trajectory of a target over a sliding window is described by a linear function of the arc length.To determine the unknown target trajectory,an augmented system is derived by denoting the unknown coefficients of the function as states in mileage coordinates.At every estimation cycle except the first one,the interaction(mixing)stage of the proposed algorithm starts from the latest estimated base state and a recalculated parameter vector,which is determined by the least squares(LS).Numerical experiments are conducted to assess the performance of the proposed algorithm.Simulation results show that the proposed algorithm can achieve better performance than the conventional coupled model-based algorithms in the presence of target maneuvers.

Modeling of Pervaporation Separation Benzene from Dilute Aqueous Solutions Through Polydimethylsiloxane Membranes

A modified solution-diffusion model was established based on Flory-Huggins thermodynamic theory and Fujita's free volume theory. This model was used for description of the mass transfer of removal benzene from dilute aqueous solutions through polydimethylsiloxane (PDMS) membranes. The effect of component concentration on the interaction parameter between components, that of the polymer membrane on the selectivity to benzene, and that of feed concentration and temperature on the permeation flux and separation factor of benzene/water through PDMS membranes were investigated. Calculated pervaporation fluxes of benzene and water were compared with the experimental results and were in good agreement with the experimental data.

Blind source separation based on generalized gaussian model

Since in most blind source separation(BSS)algorithms the estimations of probability density function(pdf)of sources are fixed or can only switch between one sup-Gaussian and other sub-Gaussian model,they may not be efficient to separate sources with different distributions.So to solve the problem of pdf mismatch and the separation of hybrid mixture in BSS,the generalized Gaussian model(GGM)is introduced to model the pdf of the sources since it can provide a general structure of univariate distributions.Its great advantage is that only one parameter needs to be determined in modeling the pdf of different sources,so it is less complex than Gaussian mixture model.By using maximum likelihood(ML)approach,the convergence of the proposed algorithm is improved.The computer simulations show that it is more efficient and valid than conventional methods with fixed pdf estimation.

Modeling and identification for soft sensor systems based on the separation of multi-dynamic and static characteristics

Data-driven soft sensor is an effective solution to provide rapid and reliable estimations for key quality variables online. The secondary variables affect the primary variable in considerably different speed, and soft sensor systems exhibit multi-dynamic characteristics. Thus, the first contribution is improving the model in the previous study with multi-time-constant. The characteristics-separation-based model will be identified in substep way,and the stochastic Newton recursive(SNR) algorithm is adopted. Considering the dual-rate characteristics of soft sensor systems, the proposed model cannot be identified directly. Thus, two auxiliary models are first proposed to offer the intersample estimations at each update period, based on which the improved algorithm(DAM-SNR) is derived. These two auxiliary models function in switching mechanism which has been illustrated in detail. This algorithm serves for the identification of the proposed model together with the SNR algorithm, and the identification procedure is then presented. Finally, the laboratorial case confirms the effectiveness of the proposed soft sensor model and the algorithms.

Study on the separation process of non-metallic inclusions at the steel-slag interface using water modeling

The separation process of non-metallic inclusions at the steel–slag interface was simulated by physical modeling. Three different kinds of particles (octahedral, plate-like, and spherical) and three different oils (kerosene, bean oil, and pump oil) were used to model inclusions and slags, respectively. The effects of inclusion geometry (shape and size) and slag properties (viscosity and interfacial tension) on the separation process were investigated. The results revealed that the variation of surface free energy and the viscosity of the slag are two significant factors affecting the separation process of inclusions at the steel–slag interface. The variation of surface free energy helped inclusions enter the slag phase, whereas the decrease of slag viscosity shortened the separation time. The deformation of the steel–slag interface could give rise to the resistance force, which would resist inclusions passing through the interface. A liquid film formed on the inclusion as it passed through the steel–slag interface, which might be related to the inclusion’s shape. © 2017, University of Science and Technology Beijing and Springer-Verlag Berlin Heidelberg.