The influence of lipid-extraction on theδ^(13)C of mesopelagic and demersal fish in the South China Sea:modification and application of lipid normalization models

Mesopelagic fish,the most important daily vertically migrating community in the oceans,are characterized by high lipid content which may obscure the interpretation of stable isotopes analysis.Demersal fish,which are important consumers in the food web dominated by mesopelagic fish,also have a high lipid content.Here we collected 127 fish samples from the South China Sea and evaluated the effect of lipid contents on△δ^(13)C of mesopelagic and demersal fish.In lipid-extracted mesopelagic fish,the C/N content ratio(<5.5)shows a clear correlation withΔδ^(13)C(the offset of bulk and lipid-extractedδ^(13)C values),especially in non-migratory and semi-migratory species;these values were less correlation in demersal fish.Based on our results,we suggest that mesopelagic and demersal fish in different regions of the South China Sea should be studied separately using appropriate correction models and less fit for the traditional model.Moreover,the C/N content ratio should be used cautiously for establishing the lipid normalization model,especially for the fish in migratory mesopelagic fish and demersal fish.Our results also reveal that mesopelagic fish across nearby regions could be analyzed together.The new models described here can be applied in future studies of mesopelagic and demersal fish in the South China Sea.

Comprehensive evaluation of 5G+smart distribution network based on combined weighting method-cloud model

With the large-scale application of 5G technology in smart distribution networks,the operation effects of distribution networks are not clear.Herein,we propose a comprehensive evaluation model of a 5G+smart distribution network based on the combination weighting and cloud model of the improved Fuzzy Analytic Hierarchy-Entropy Weight Method(FAHP-EWM).First,we establish comprehensive evaluation indexes of a 5G+smart distribution network from five dimensions:reliable operation,economic operation,efficient interaction,technological intelligence,and green emission reduction.Second,by introducing the principle of variance minimization,we propose a combined weighting method based on the improved FAHP-EWM to calculate the comprehensive weight,so as to reduce the defects of subjective arbitrariness and promote objectivity.Finally,a comprehensive evaluation model of 5G+smart distribution network based on cloud model is proposed by considering the uncertainty of distribution network node information and equipment status information.The example analysis indicates that the overall operation of the 5G+smart distribution network project is decent,and the weight value calculated by the combined weighting method is more reasonable and accurate than that calculated by the single weighting method,which verifies the effectiveness and rationality of the proposed evaluation method.Moreover,the proposed evaluation method has a certain guiding role for the large-scale application of 5G communication technology in smart distribution networks.

Finite Mixture Normal Models, with Application to Dose-Response Studies

In this paper, we consider the risk assessment problem under multi-levels and multiple mixture subpopulations. Our result is the generalization of the results of [1-5].1 Finite Mixture Normal ModelsIn dose-response studies, a class of phenomena that frequently occur are that experimental subjects (e.g., mice) may have different responses like ’none, mild, severe’ after a toxicant experiment, or ’getting worse, no change, getting better’ after a medical treatment, etc. These phenomena have attracted the attention of many researchers in recent years. Finite

EQUIVALENT NORMAL CURVATURE APPROACH MILLING MODEL OF MACHINING FREEFORM SURFACES

A new milling methodology with the equivalent normal curvature milling model machining freeform surfaces is proposed based on the normal curvature theorems on differential geometry. Moreover, a specialized whirlwind milling tool and a 5-axis CNC horizontal milling machine are introduced. This new milling model can efficiently enlarge the material removal volume at the tip of the whirlwind milling tool and improve the producing capacity. The machining strategy of this model is to regulate the orientation of the whirlwind milling tool relatively to the principal directions of the workpiece surface at the point of contact, so as to create a full match with collision avoidance between the workpiece surface and the symmetric rotational surface of the milling tool. The practical results show that this new milling model is an effective method in machining complex three- dimensional surfaces. This model has a good improvement on finishing machining time and scallop height in machining the freeform surfaces over other milling processes. Some actual examples for manufacturing the freeform surfaces with this new model are given.

An improved model of partition curve based on accumulation normal distribution function

Extensive studies based on partition curve of gravity separation have been investigated. All created models are merely used to simulate density distribution at the same size fraction. However, they cannot be used to predictive distribution of materials depending on compound feature of density and size. According to this situation, an improved model of partition curve based on accumulation normal distribution, which was distinguished from conventional model of accumulation normal distribution for partition curve, was proposed in this paper. It could simulate density distribution at different size fractions by using the density-size compound index and conflating the partition curves at different size fractions as one partition curve. The feasibility of three compound indexes, including mass index, settlement index and transformation index, were investigated. Specific forms of the improved model were also proposed. It is found that transformation index leads to the best fitting results, while the fitting error is only 1.75 according to the fitting partition curve.

Experimental and modeling study of kinetics for methane hydrate formation in a crude oil-in-water emulsion

A low-viscosity emulsion of crude oil in water can be believed to be the bulk of a flow regime in a pipeline.To differentiate which crude oil would and which would not counter the blockage of flow due to gas hydrate formation in flow channels,varying amount of crude oil in water emulsion without any other extraneous additives has undergone methane gas hydrate formation in an autoclave cell.Crude oil was able to thermodynamically inhibit the gas hydrate formation as observed from its hydrate stability zone.The normalized rate of hydrate formation in the emulsion has been calculated from an illustrative chemical affinity model,which showed a decrease in the methane consumption（decreased normalized rate constant） with an increase in the oil content in the emulsion.Fourier transform infrared spectroscopy（FTIR） of the emulsion and characteristic properties of the crude oil have been used to find the chemical component that could be pivotal in selfinhibitory characteristic of the crude oil collected from Ankleshwar,India,against a situation of clogged flow due to formation of gas hydrate and establish flow assurance.

Joint modelling of location and scale parameters of the skew-normal distribution

Joint location and scale models of the skew-normal distribution provide useful ex- tension for joint mean and variance models of the normal distribution when the data set under consideration involves asymmetric outcomes. This paper focuses on the maximum likelihood estimation of joint location and scale models of the skew-normal distribution. The proposed procedure can simultaneously estimate parameters in the location model and the scale model. Simulation studies and a real example are used to illustrate the proposed methodologies.

An Efficient Three-Dimensional Coupled Normal Mode Model and Its Application to Internal Solitary Wave Problems

We present an efficient three-dimensional coupled-mode model based on the Fourier synthesis technique. In principle, this model is a one-way model, and hence provides satisfactory accuracy for problems where the forward scattering dominates. At the same time, this model provides an efficiency gain of an order of magnitude or more over two-way coupled-mode models. This model can be applied to three-dimensional range-dependent problems with a slowly varying bathymetry or internal waves. A numerical example of the latter is demonstrated in this work. Comparisons of both accuracy and efficiency between the present model and a benchmark model are also provided.

A theoretical model of collision between soft-spheres with Hertz elastic loading and nonlinear plastic unloading

This paper presents a theoretical model on the normal(head-on) collision between soft-spheres on the basis of elastic loading of the Hertz contact for compression process and a nonlinear plastic unloading for restitution one,in which the parameters all are determined in terms of the material and geometric ones of the spheres,and the behaviors of perfect elastic,inelastic,and perfect plastic collisions appeared in the classical mechanics are fully described once a value of coefficient of restitution is specified in the region of 0 ≤ ε ≤ 1.After an empirical formula of the coefficient of restitution dependent on the impact velocity is suggested to fit the existing experimental measurements by means of the least square method,the predictions of the dependency and the collision duration are in well quantitative agreement with their experimental measurements.It is found that the measurable quantities are dependent on both the impact velocity and the parameters of spheres.Following this model,finally,an approach to determine the spring coefficient in the linear viscoelastic model of the collision is also displayed.These results obtained here will be significantly beneficial for the applications where a collision model is requested in the simulations of relevant grain flows and impact dynamics etc..

Risk transmission evaluation for parallel construction of warships based on IFCM and the cloud model

To cope with multi-directional transmission coupling,spreading, amplification, and chain reaction of risks during multiproject parallel construction of warships, a risk transmission evaluation method is proposed, which integrates an intuitionistic cloud model with a fuzzy cognitive map. By virtue of expectancy Ex, entropy En, and hyper entropy He, the risk fuzziness and randomness of the knowledge of experts are organically combined to develop a method for converting bi-linguistic variable decision-making information into the quantitative information of the intuitionistic normal cloud(INC) model. Subsequently, the threshold function and weighted summation operation in the traditional fuzzy cognitive map is converted into the INC ordered weighted averaging operator to create the risk transmission model based on the intuitionistic fuzzy cognitive map(IFCM) and the algorithm for solving it. Subsequently, the risk influence sequencing method based on INC and the risk rating method based on nearness are proposed on the basis of Monte Carlo simulation in order to realize the mutual conversion of the qualitative and quantitative information in the risk evaluation results.Example analysis is presented to verify the effectiveness and practicality of the methods.

Analytical Higher-Order Model for Flexible and Stretchable Sensors

The stretchable sensor wrapped around a foldable airfoil or embedded inside of it has great potential for use in the monitoring of the structural status of the foldable airfoil.The design methodology is important to the development of the stretchable sensor for status monitoring on the foldable airfoil.According to the requirement of mechanical flexibility of the sensor,the combined use of a layered flexible structural formation and a strain isolation layer is implemented.An analytical higher-order model is proposed to predict the stresses of the strain-isolation layer based on the shear-lag model for the safe design of the flexible and stretchable sensors.The normal stress and shear stress equations in the constructed structure of the sensors are obtained by the proposed model.The stress distribution in the structure is investigated when bending load is applied to the structures.The numerical results show that the proposed model can predict the variation of normal stress and shear stress along the thickness of the strain-isolation（polydimethylsiloxane）layer accurately.The results by the proposed model are in good agreement with the finite element method,in which the normal stress is variable while the shear stress is invariable along the thickness direction of strain-isolation layer.The high-order model is proposed to predict the stresses of the layered structure of the flexible and stretchable sensor for monitoring the status of the foldable airfoil.

A Recommendation Method for Highly Sparse Dataset Based on Teaching Recommendation Factorization Machines

There is no reasonable scientific basis for selecting the excellent teachers of the school’s courses.To solve the practical problem,we firstly give a series of normalization models for defining the key attributes of teachers’professional foundation,course difficulty coefficient,and comprehensive evaluation of teaching.Then,we define a partial weight function to calculate the key attributes,and obtain the partial recommendation values.Next,we construct a highly sparse Teaching Recommendation Factorization Machines(TRFMs)model,which takes the 5-tuples relation including teacher,course,teachers’professional foundation,course difficulty,teaching evaluation as the feature vector,and take partial recommendation value as the recommendation label.Finally,we design a novel Top-N excellent teacher recommendation algorithm based on TRFMs by course classification on the highly sparse dataset.Experimental results show that the proposed TRFMs and recommendation algorithm can accurately realize the recommendation of excellent teachers on a highly sparse historical teaching dataset.The recommendation accuracy is superior to that of the three-dimensional tensor decomposition model algorithm which also solves sparse datasets.The proposed method can be used as a new recommendation method applied to the teaching arrangements in all kinds of schools,which can effectively improve the teaching quality.

L-two-optimal identification of errors-in-variables models:a frequency-domain approach

This paper proposes an L-two-optimal identification approach to cope with errors-in-variables model （EIVM） identification. With normalized coprime factor model （NCFM） representations, L-two-optimal approximate models are derived from the framework of an EIVM according to the kernel and image representations of related signals. Based on the optimal approximate models, the v-gap metric is employed as a minimization criterion to optimize the parameters of a system model, and thus the resulting optimization problem can be solved by linear matrix inequalities （LMIs）. In terms of the optimized system model, the noise model （NM） can be readily obtained by right multiplication of an inner. Compared with other EIVM identification methods, the proposed one has a wider scope of applications because the statistical properties of disturbing noises are not demanded. It is also capable of giving identifiabiUty. Finally, a numerical simulation is used to verify the effectiveness of the proposed method.

Evaluation of Burden Descent Model for Burden Distribution in Blast Furnace

Mathematical models for burden descending process have been applied to obtain whole burden structures in blast furnace,whereas the accuracy of those burden descent models has not been sufficiently investigated.Special evaluation method based on timeline burden profiles was established to quantitatively evaluate the error between experimental and modeled burden structures.Four existing burden descent models were utilized to describe the burden structure of a 1/20 scaled warm blast furnace.Input modeling conditions including initial burden profile,descending volumes in each time interval,and normalized descending velocity distribution were determined via special image processing technology.Modeled burden structures were evaluated combined with the published experimental data.It is found that all the models caught the main profile of the burden structure.Furthermore,the improved nonuniform descent model（Model IV）shows the highest level of precision especially when burden descends with unstable velocity distribution tendency.Meanwhile,the traditional nonuniform descent model（Model III）may also be desirable to model the burden descending process when the burden descending velocity presents a linear tendency.Finally,the uniform descent model（Model I）might be the first option for roughly predicting burden structure.

Risk estimation based on mixed normal distribution model for diabetes-related hospitalization claims

Medical insurance service, the important part of national healthcare supporting system with a history dating back more than 100 years ago, remains a global challenge because of its high rates of compensation and difficulty in risk control. When developing the diabetes related, hospitalization insurance, we found that the risk loss of the diabetic inpatients does not follow a symmetrical unimodal distribution： in fact, it is hard to describe its risk loses distribution with a single probability distribution model. Therefore, we put forward a risk measurement method based on a mixed normal distributions model for medical insurance of inpatients with diabetes.

The Minimax Estimator of Stochastic Regression Coefficients and Parameters in the Class of All Estimators

In this paper, the authors address the problem of the minimax estimator of linear combinations of stochastic regression coefficients and parameters in the general normal linear model with random effects. Under a quadratic loss function, the minimax property of linear estimators is investigated. In the class of all estimators, the minimax estimator of estimable functions, which is unique with probability 1, is obtained under a multivariate normal distribution.

Numerical study of roll motion of a 2-D floating structure in viscous flow

In the present study,an open source CFD tool,Open FOAM has been extended and applied to investigate roll motion of a 2-D rectangular barge induced by nonlinear regular waves in viscous flow.Comparisons of the present Open FOAM results with published potential-flow solutions and experimental data have indicated that the newly extended Open FOAM model is very capable of accurate modelling of wave interaction with freely rolling structures.The wave-induced roll motions,hydrodynamic forces on the barge,velocities and vorticity fields in the vicinity of the structure in the presence of waves have been investigated to reveal the real physics involved in the wave induced roll motion of a 2-D floating structure.Parametric analysis has been carried out to examine the effect of structure dimension and body draft on the roll motion.

LARGE EDDY SIMULATION (LES) OF TURBULENT FLOW BY FINITE DIFFERENCE METHOD

A Large Eddy Simulation (LES) technique was applied to solve the turbulentchannel flow for Re_τ = 150 . Three types of turbulence models are employed, such as theSmagorinsky model, the Dynamic Sub-Grid Scale(SGS) model and the Generalized Normal Stress (GNS)model. The simulated data in time series for the LES were averaged in both time and space to carryout the statistical analysis. Results of LES were compared with that of a DNS. As an application, aLES technique was used for 2D body in order to check the validation by investigating the turbulentvortical motion around the afterbody with a slant angle.

Summaries of three keynote lectures at the SAE–2018

Three keynote lectures are presented at the conference of Small Area Estimation and OtherTopics of Current Interest in Surveys, Official Statistics and General Statistics (SAE 2018), an international conference held between June 16 and 18 at East China Normal University, Shanghai,China. The speakers of these lectures are world famous statistics professors, James O. Berger,J. N. K. Rao and Malay Ghosh. The lectures mainly review the previous studies and present thepioneering results covering Bayesian statistics, small area estimation, shrinkage priors, etc.