Global Analysis of a Flexible Riser

The mechanical performance of a flexible riser is more outstanding than other risers in violent environmental conditions. Based on the lumped mass method, a steep wave flexible riser configuration attached to a Floating Production Storage and Offioading （FPSO） has been applied to a global analysis in order to acquire the static and dynamic behavior of the flexible riser. The riser was divided into a series of straight massless line segments with a node at each end. Only the axial and torsional properties of the line were modeled, while the mass, weight, and buoyancy were all lumped to the nodes. Four different buoyancy module lengths have been made to demonstrate the importance of mode selection, so as to confirm the optimum buoyancy module length. The results in the sensitivity study show that the flexible riser is not very sensitive to the ocean current, and the buoyancy module can reduce the Von Mises stress and improve the mechanical performance of the flexible riser. Shorter buoyancy module length can reduce the riser effective tension in a specific range of the buoyancy module length when other parameters are constant, but it can also increase the maximum curvature of the riser. As a result, all kinds of the riser performances should be taken into account in order to select the most appropriate buoyancy module length.

A Preliminary Global Analysis of Remotely Sensed Radiation Characteristics of Ground Features:Ⅱ. Result Discussion

Based on the L,B and V statistics of the 106 ground feature groups or the 769 ground feature class units in the world presented in the part I of the paper,the distribution of the world ground features on the axes of L,B and V,in the planes of L-B,L-V and B-V and in the space of L-B-V was discussed.And the typical numerical characteristics of the various ground features were also summarized.

A global analysis searching for neutrinos associated with black hole merger gravitational wave events

Several neutrino observatories have searched for coincident neutrino signals associated with gravitational waves induced by the merging of two black holes. No statistically significant neutrino signal in excess of the background level was observed. These experiments use different neutrino detection technologies and are sensitive to various neutrino types. A combined analysis was performed on the KamLAND, Super-Kamiokande and Borexino experimental data with a frequentist statistical approach to achieve a global picture of the associated neutrino fluence. Both monochromatic and Fermi-Dirac neutrino spectra were assumed in the calculation. The final results are consistent with null neutrino signals associated with the process of a binary black hole merger. The derived 90% confidence level upper limits on the fluence and luminosity of various neutrino types are presented for neutrino energy less than110 MeV.

A two scaled numerical method for global analysis of high dimensional nonlinear systems

This paper first analyzes the features of two classes of numerical methods for global analysis of nonlinear dynamical systems, which regard state space respectively as continuous and discrete ones. On basis of this understanding it then points out that the previously proposed method of point mapping under cell reference (PMUCR), has laid a frame work for the development of a two scaled numerical method suitable for the global analysis of high dimensional nonlinear systems, which may take the advantages of both classes of single scaled methods but will release the difficulties induced by the disadvantages of them. The basic ideas and main steps of implementation of the two scaled method, namely extended PMUCR, are elaborated. Finally, two examples are presented to demonstrate the capabilities of the proposed method.

Global analysis of sensitivity of bioretention cell design elements to hydrologic performance

Analysis of sensitivity of bioretention cell design elements to their hydrologic performances is meaningful in offering theoretical guidelines for proper design. Hydrologic performance of bioretention cells was facilitated with consideration of four metrics： the overflow ratio, groundwater recharge ratio, ponding time, and runoff coefficients. The storm water management model （SWMM） and the bioretention infiltration model RECARGA were applied to generating runoff and outflow time series for calculation of hydrologic performance metrics. Using a parking lot to build a bioretention cell, as an example, the Morris method was used to conduct global sensitivity analysis for two groups of bioretention samples, one without underdrain and the other with underdrain. Results show that the surface area is the most sensitive element to most of the hydrologic metrics, while the gravel depth is the least sensitive element whether bioretention cells are installed with underdrain or not. The saturated infiltration rate of planting soil and the saturated infiltration rate of native soil are the other two most sensitive elements for bioretention cells without underdrain, while the saturated infiltration rate of native soil and underdrain size are the two most sensitive design elements for bioretention cells with underdrain.

A Bayesian multi-model inference methodology for imprecise momentindependent global sensitivity analysis of rock structures

Traditional global sensitivity analysis(GSA)neglects the epistemic uncertainties associated with the probabilistic characteristics(i.e.type of distribution type and its parameters)of input rock properties emanating due to the small size of datasets while mapping the relative importance of properties to the model response.This paper proposes an augmented Bayesian multi-model inference(BMMI)coupled with GSA methodology(BMMI-GSA)to address this issue by estimating the imprecision in the momentindependent sensitivity indices of rock structures arising from the small size of input data.The methodology employs BMMI to quantify the epistemic uncertainties associated with model type and parameters of input properties.The estimated uncertainties are propagated in estimating imprecision in moment-independent Borgonovo’s indices by employing a reweighting approach on candidate probabilistic models.The proposed methodology is showcased for a rock slope prone to stress-controlled failure in the Himalayan region of India.The proposed methodology was superior to the conventional GSA(neglects all epistemic uncertainties)and Bayesian coupled GSA(B-GSA)(neglects model uncertainty)due to its capability to incorporate the uncertainties in both model type and parameters of properties.Imprecise Borgonovo’s indices estimated via proposed methodology provide the confidence intervals of the sensitivity indices instead of their fixed-point estimates,which makes the user more informed in the data collection efforts.Analyses performed with the varying sample sizes suggested that the uncertainties in sensitivity indices reduce significantly with the increasing sample sizes.The accurate importance ranking of properties was only possible via samples of large sizes.Further,the impact of the prior knowledge in terms of prior ranges and distributions was significant;hence,any related assumption should be made carefully.

GLOBAL ANALYSIS OF A CLASS OF CUBIC SYSTEMS

The paper gives a complete golbal phase plane analysis of the cubic system x = y ax2-dx3,y=-x + bx2 + x3, which arises in the study of the structure of a three-dimensional viscous flow near a plane wall.

A generalized preimage theorem in global analysis

The concept of locally fine point and generalized regular valueof a C1 map between Banach spaces were carried over C1 map between Banach manifolds. Hence the preimage theorem, a principle constructing Banach manifolds in global analysis, is generalized.

Global analysis of smooth solutions to a hyperbolic-parabolic coupled system

We investigate a model arising from biology, which is a hyperbolic- parabolic coupled system. First, we prove the global existence and asymptotic behavior of smooth solutions to the Cauchy problem without any smallness assumption on the initial data. Second, if the Hs ∩ Ll-norm of initial data is sufficiently small, we also establish decay rates of the global smooth solutions. In particular, the optimal L2 decay rate of the solution and the almost optimal L2 decay rate of the first-order derivatives of the solution are obtained. These results are obtained by constructing a new nonnegative convex entropy and combining spectral analysis with energy methods.

GLOBAL ANALYSIS OF THE METABOLITES OF CURCUMIN IN RATS BY ULTRA PERFORMANCE LIQUID CHROMATOGRAPHY COUPLED WITH QUADRUPOLE TIME OF-FLIGHT TANDEM MASS SPECTROMETRY

Curcumin,a safe natural yellow pigment with a wide range of pharmacological activities,is used both in herbal drugs and as a food coloring agents.Studies have shown that curcumin would suffer from extensive metabolism in vivoand the predominant metabolic pathways are reduction and conjugation.In order to comprehensively study the metabolism and enrich the metabolic profile of cxurcumin in vivo,we carried out this research.A systematic method with highly sensitive UPLC-Q/TOF-MS was established to analyze different biological samples of rats after

Parameter identification and global sensitivity analysis of Xin'anjiang model using meta-modeling approach

Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on （1） a screening method （Morris） for qualitative ranking of parameters, and （2） a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model （RSMSobol）. First, the Morris screening method was used to qualitatively identify the parameters＇ sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model （RSM） were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.

Global Geometric Analysis of Ship Rolling and Capsizing in Random Waves

The nonlinear biased ship rolling motion and capsizing in randoro waves are studied by utilizing a global geometric method. Thompson＇ s α-parameterized family of restoring functions is adopted in the vessel equation of motion for the representation of bias. To take into account the presence of randomness in the excitation and the response, a stochastic Melnikov method is developed and a mean-square criterion is obtained to provide an upper bound on the domain of the potential chaotic rolling motion. This criterion can be used to predict the qualitative nature of the invariant manifolds which represent the boundary botween safe and unsafe initial conditions, and how these depend on system parameters of the specific ship model. Phase space transport theory and lobe dynamics are used to demonstrate how motions starting from initial conditions inside the regions bounded by the intersected manifolds will evolve and how unexpected capsizing can occur.

Global Analysis of a Class of Cooperative Systems

In this paper we analyze globally the behavior of the solutions of a class of cooperative systems. Our main results is that every orbit of the cooperative system (3.1) either approaches the equilibrium (0, 0, 0), or is unbounded, ast→+∞.

Global sensitivity analysis based on high-dimensional sparse surrogate construction

Surrogate models are usually used to perform global sensitivity analysis （GSA） by avoiding a large ensemble of deterministic simulations of the Monte Carlo method to provide a reliable estimate of GSA indices. However, most surrogate models such as polynomial chaos （PC） expansions suffer from the curse of dimensionality due to the high-dimensional input space. Thus, sparse surrogate models have been proposed to alleviate the curse of dimensionality. In this paper, three techniques of sparse reconstruc- tion are used to construct sparse PC expansions that are easily applicable to computing variance-based sensitivity indices （Sobol indices）. These are orthogonal matching pursuit （OMP）, spectral projected gradient for L1 minimization （SPGL1）, and Bayesian compressive sensing with Laplace priors. By computing Sobol indices for several benchmark response models including the Sobol function, the Morris function, and the Sod shock tube problem, effective implementations of high-dimensional sparse surrogate construction are exhibited for GSA.

Moment-independence global sensitivity analysis for the system with fuzzy failure state and its Kriging method

For the system with the fuzzy failure state, the effects of the input random variables and the fuzzy failure state on the fuzzy probability of failure for the structural system are studied, and the moment-independence global sensitivity analysis（GSA） model is proposed to quantitatively measure these effects. According to the fuzzy random theory, the fuzzy failure state is transformed into an equivalent new random variable for the system, and the complementary function of the membership function of the fuzzy failure state is defined as the cumulative distribution function（CDF） of the new random variable. After introducing the new random variable, the equivalent performance function of the original problem is built. The difference between the unconditional fuzzy probability of failure and conditional fuzzy probability of failure is defined as the moment-independent GSA index. In order to solve the proposed GSA index efficiently, the Kriging-based algorithm is developed to estimate the defined moment-independence GSA index. Two engineering examples are employed to verify the feasibility and rationality of the presented GSA model, and the advantages of the developed Kriging method are also illustrated.

Automatic Satisfaction Analysis in Call Centers Considering Global Features of Emotion and Duration

Analysis of customers＇ satisfaction provides a guarantee to improve the service quality in call centers.In this paper,a novel satisfaction recognition framework is introduced to analyze the customers＇ satisfaction.In natural conversations,the interaction between a customer and its agent take place more than once.One of the difficulties insatisfaction analysis at call centers is that not all conversation turns exhibit customer satisfaction or dissatisfaction. To solve this problem,an intelligent system is proposed that utilizes acoustic features to recognize customers＇ emotion and utilizes the global features of emotion and duration to analyze the satisfaction. Experiments on real-call data show that the proposed system offers a significantly higher accuracy in analyzing the satisfaction than the baseline system. The average F value is improved to 0. 701 from 0. 664.

Effects of Initial and Boundary Conditions on Heavy Rainfall Simulation over the Yellow Sea and the Korean Peninsula:Comparison of ECMWF and NCEP Analysis Data Effects and Verification with Dropsonde Observation

This study evaluated the simulation performance of mesoscale convective system(MCS)-induced precipitation,focusing on three selected cases that originated from the Yellow Sea and propagated toward the Korean Peninsula.The evaluation was conducted for the European Centre for Medium-Range Weather Forecasts(ECMWF)and National Centers for Environmental Prediction(NCEP)analysis data,as well as the simulation result using them as initial and lateral boundary conditions for the Weather Research and Forecasting model.Particularly,temperature and humidity profiles from 3D dropsonde observations from the National Center for Meteorological Science of the Korea Meteorological Administration served as validation data.Results showed that the ECMWF analysis consistently had smaller errors compared to the NCEP analysis,which exhibited a cold and dry bias in the lower levels below 850 hPa.The model,in terms of the precipitation simulations,particularly for high-intensity precipitation over the Yellow Sea,demonstrated higher accuracy when applying ECMWF analysis data as the initial condition.This advantage also positively influenced the simulation of rainfall events on the Korean Peninsula by reasonably inducing convective-favorable thermodynamic features(i.e.,warm and humid lower-level atmosphere)over the Yellow Sea.In conclusion,this study provides specific information about two global analysis datasets and their impacts on MCS-induced heavy rainfall simulation by employing dropsonde observation data.Furthermore,it suggests the need to enhance the initial field for MCS-induced heavy rainfall simulation and the applicability of assimilating dropsonde data for this purpose in the future.

Analysis and Investigation on the Core Competitiveness of Textile and Underwear Industry in the Mainland and Taiwan of China and Myanmar under the Global Value Chain

All the way in the “area”, “comprehensive economic partnership agreement” (RCEP), Association of Southeast Asian Nations (ASEAN) free trade area, the main economic corridor construction under the background of success, the mainland and Taiwan of China and southeast Asia has established the important relations of cooperation, industries are beginning to consider labor costs, raw materials, using the regional to invest policy and market comparative advantage. This paper starts from the investigation of Topline’s core competitiveness in China and Myanmar, and focuses on the analysis of lingerie industry in China and Myanmar, and the analysis of women’s underwear industry from the perspective of global value chain (GVC). Through the data analysis of the questionnaire survey, this paper summarizes the problems existing in the current situation of the industry, uses the intermediary analysis to analyze the correlation between the two variables, reveals the role of the core competitiveness of enterprises, and uses the GVC theory to analyze the problems existing in the industry of enterprises and their causes. According to relevant theories, the optimization path of enterprise value chain is put forward.

Developing global image feature analysis models to predict cancer risk and prognosis

In order to develop precision or personalized medicine,identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently.Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images,which include steps in detecting and segmenting suspicious regions or tumors,followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors.However,due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors,segmenting subtle regions is often difficult and unreliable.Additionally,ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches.In our recent studies,we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis.We trained and tested several models using images obtained from full-field digital mammography,magnetic resonance imaging,and computed tomography of breast,lung,and ovarian cancers.Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice.Furthermore,the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis.Therefore,the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power.