Managing uncertainty and discontinuous condition numbers in geometric computation

We consider operations on subdivision surfaces under the strict robustness requirement that these floating-point computations return an object with the same topological form as the true solution. The problems involved may however be ill-conditioned, and defined in terms of uncertain data, and even supplementary interval arithmetic may not ensure robustness. Trapping mechanisms are therefore proposed to resolve this difficulty.     

Exploring the boundary region of tolerance rough sets for feature selection

Of all of the challenges which face the effective application of computational intelligence technologies for pattern recognition, dataset dimensionality is undoubtedly one of the primary impediments. In order for pattern classifiers to be efficient, a dimensionality reduction stage is usually performed prior to classification. Much use has been made of rough set theory for this purpose as it is completely data-driven and no other information is required; most other methods require some additional knowledge. However, traditional rough set-based methods in the literature are restricted to the requirement that all data must be discrete. It is therefore not possible to consider real-valued or noisy data. This is usually addressed by employing a discretisation method, which can result in information loss. This paper proposes a new approach based on the tolerance rough set model, which has the ability to deal with real-valued data whilst simultaneously retaining dataset semantics. More significantly, this paper describes the underlying mechanism for this new approach to utilise the information contained within the boundary region or region of uncertainty. The use of this information can result in the discovery of more compact feature subsets and improved classification accuracy. These results are supported by an experimental evaluation which compares the proposed approach with a number of existing feature selection techniques.     

Effect of ionic strength on the reactivity ratios of acrylamide/acrylic acid (sodium acrylate) copolymerization

The ionic strength (IS) of polyelectrolyte solutions plays an important role in influencing reaction kinetics. The largely unstudied effect of IS on monomer reactivity ratios and copolymerization rates of acrylamide (AAm) and acrylic acid (AAc), in the form of sodium acrylate (NaAc), is investigated. Salt addition affects the nature of overall charges of the polyelectrolyte solution and diminishes the electrostatic repulsions between reacting chains. Therefore, changing the IS of the solution by incorporating salts affect not only the point estimates of the monomer reactivity ratios but also the overall behavior of the copolymerization (with a transition to azeotropic behavior). Experimental results on copolymerization rates confirm the observed trends in reactivity ratio behavior. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 40949.     

地球村里的食品安全——跨国企业的产品检测挑战

近些年,检测部门对食品生产和制药行业的质量安全的关注越来越密切,并且关注程度还在不断加大。如果品牌所有者的产品被发现带有异物污染,如玻璃或金属碎片,则会遵照各个国家相关部门的规定,如英国的食品标准局(FSA)和美国食品药品管理局(FDA)进行成本高昂和损害声誉的产品召回。仅在2012年2月在FDA的网站上就列出了大约     

Tubular shaped composites made from polythiophene covalently linked to Prato functionalized N-doped carbon nanotubes

A simple methodology to produce tubular nitrogen-doped carbon nanotube/polythiophene covalently linked composites is described. Nitrogen doped carbon nanotubes (N-CNTs) were made by the floating catalyst CVD method using toluene, ferrocene and tetramethylethylenediamine (TMEDA) as reagents. Functionalization of the N-CNTs was achieved using 3-thiophenecarboxaldehyde and N-methylglycine in 1,2-dichlorobenzene (Prato reaction). Elemental analysis showed nitrogen incorporation of N into the N-CNTs (1.8%) and also the N-methylglycine functionalized N-CNTs (f-N-CNTs; 6.2%). A series of f-N-CNT/thiophene monomer mixtures (weight ratios 1:3, 1:10 and 1:20) were used to make f-N-CNT/polythiophene tubular composites. As the amount of thiophene monomer was increased, the overall diameter of the polymer layer attached onto the N-CNTs increased. Polymer thickness also varied with reaction time (1 h, 12 h and 24 h). The combination of acid functionalization and N–doping gives the best coverage of the CNTs by polythiophene, in which the polythiophene preferentially binds to the f-N-CNTs to give tubular structures.     

Modeling of an Unbonded CFRP Strap Shear Retrofitting System for Reinforced Concrete Beams

A retrofitting technique has been developed that uses carbon fiber-reinforced polymer (CFRP) straps to increase the shear capacity of reinforced concrete beams. The vertical straps are not bonded to the beam but are instead anchored against the beam, which makes this technique potentially more effective than bonded FRP retrofitting techniques. However, it also means that models for bonded FRPs are not appropriate for use with the straps. Instead, a model based on a shear friction approach has been developed where the strain in the straps is calculated based on a term that accounts for the effects of prestress and additional strain in the strap due to shear crack opening. The model can either consider the shear reinforcement to be smeared along the length of the beam or discrete elements. The “smeared” model was checked against an experimental database consisting of rectangular, T-, and deep beams, both in terms of predicted capacity and predicted strain in the straps. Overall the smeared model predicted the capacity of the specimens and, with some adjustments, the strains quite accurately. There were, however, cases when it was more appropriate to use the “discrete” model such as when the transverse reinforcement ratio was low or when the transverse reinforcement spacing was high. Further experimental data are required to fully validate the models and to determine appropriate limits on the use of the smeared model and the discrete model. However, the initial results are promising.     

Probabilistic uncertainty analysis of an FRF of a structure using a Gaussian process emulator

This paper introduces methods for probabilistic uncertainty analysis of a frequency response function (FRF) of a structure obtained via a finite element (FE) model. The methods are applicable to computationally expensive FE models, making use of a Bayesian metamodel known as an emulator. The emulator produces fast predictions of the FE model output, but also accounts for the additional uncertainty induced by only having a limited number of model evaluations. Two approaches to the probabilistic uncertainty analysis of FRFs are developed. The first considers the uncertainty in the response at discrete frequencies, giving pointwise uncertainty intervals. The second considers the uncertainty in an entire FRF across a frequency range, giving an uncertainty envelope function. The methods are demonstrated and compared to alternative approaches in a practical case study.