Acrylamide and sodium acrylate are copolymerized in aqueous solution to study the influence of monomer concentration and ionic strength onto the reactivity ratios using in-situ H NMR. Increasing the monomer content leads to larger reactivity of the ionized monomer. At low monomer concentration, this effect was reproduced by adding NaCl to increase the ionic strength, indicating that the reaction kinetics is largely governed by charge interactions. On the contrary, this was not observed at higher monomer content, suggesting that non-electrostatic effects are mainly responsible of the monomer concentration dependence at these conditions. A comprehensive mathematical model was developed to predict copolymer composition as a function of monomer concentration and ionic strength. It is based on a previously-proposed rate law of propagation for ionized monomers, which has been expanded to cover any ionization degree of acrylic acid. The model is capable to reproduce composition data from different sources obtained in a wide range of reaction conditions.
Some Antarctic littorinoideans have a remarkable convergence with Naticoidea in shell and operculum features. Two naticid-like species of that group are studied in their phenotypic features in order to improve their taxonomy and to discuss the meaning of that convergence, as the former are herbivore-detritivore and the latter active predatory organisms. One of the studied species is the littorinid Laevilacunaria antarctica (Martens, 1885). The other belongs to a new genus - Pseudonatica, with the type species also newly described: P. antarctica, the genus is tentatively placed in Zerotulidae. Another Pseudonatica is also described, P. ampullarica, based only on shells collected by Marion-Dufresne French expedition off Brazilian coast, this finding expands the occurrence of zerotulids northwards. Besides the similarities of shell and operculum, other structures of these Antarctic species also show singular similarities with naticoideans, such as the wide foot, the complexity of opercular attachment in pedal opercular pad, the wide oesophageal gland, and the coiled arrangement of the pallial oviduct. The phenotypic characters were coded and inserted in a previous large phylogenetic analysis on Caenogastropoda (Simone, 2011), furnishing a wide basis for discussion on the characters, taxonomic position, evolution and adaptations of these organisms.
Conventional methods of matrix completion are linear methods that are not effective in handling data of nonlinear structures. Recently a few researchers attempted to incorporate nonlinear techniques into matrix completion but there still exists considerable limitations. In this paper, a novel method called deep matrix factorization (DMF) is proposed for nonlinear matrix completion. Different from conventional matrix completion methods that are based on linear latent variable models, DMF is on the basis of a nonlinear latent variable model. DMF is formulated as a deep-structure neural network, in which the inputs are the low-dimensional unknown latent variables and the outputs are the partially observed variables. In DMF, the inputs and the parameters of the multilayer neural network are simultaneously optimized to minimize the reconstruction errors for the observed entries. Then the missing entries can be readily recovered by propagating the latent variables to the output layer. DMF is compared with state-of-the-art methods of linear and nonlinear matrix completion in the tasks of toy matrix completion, image inpainting and collaborative filtering. The experimental results verify that DMF is able to provide higher matrix completion accuracy than existing methods do and DMF is applicable to large matrices.
Operational risk refers to deficiencies in processes, systems, people or external events, which may generate losses for an organization. The Basel Committee on Banking Supervision has defined different possibilities for the measurement of operational risk, although financial institutions are allowed to develop their own models to quantify operational risk. The advanced measurement approach, which is a risk-sensitive method for measuring operational risk, is the financial institutions preferred approach, among the available ones, in the expectation of having to hold less regulatory capital for covering operational risk with this approach than with alternative approaches. The advanced measurement approach includes the loss distribution approach as one way to assess operational risk. The loss distribution approach models loss distributions for business-line-risk combinations, with the regulatory capital being calculated as the 99,9% operational value at risk, a percentile of the distribution for the next year annual loss. One of the most important issues when estimating operational value at risk is related to the structure (type of distribution) and shape (long tail) of the loss distribution. The estimation of the loss distribution, in many cases, does not allow to integrate risk management and the evolution of risk; consequently, the assessment of the effects of risk impact management on loss distribution can take a long time. For this reason, this paper proposes a flexible integrated inverse adaptive fuzzy inference model, which is characterized by a Monte-Carlo behavior, that integrates the estimation of loss distribution and different . This new model allows to see how the management of risk of an organization can evolve over time and it effects on the loss distribution used to estimate the operational value at risk. The experimental study results, reported in this paper, show the flexibility of the model in identifying (1) the structure and shape of the fuzzy input sets that represent the frequency and severity of risk; and (2) the risk profile of an organization. Therefore, the proposed model allows organizations or financial entities to assess the evolution of their risk impact management and its effect on loss distribution and operational value at risk in real time.
This research described fossil woods with preserved pith and primary xylem from strata of the Irati Formation (Artinskian) of the Paraná Basin (southeastern Brazil). The three specimens studied are all related to the Coniferales and include the following taxa: Solenopitys paulistana Kräusel, Atlanticoxylon ibiratinum n. sp. and Abietopitys sp. Kräusel. Solenopitys paulistana represents a species with a solid, heterocellular pith with a peripheral secretory canal system. Atlanticoxylon ibiratinum n. sp. has a lacunose, heterocelular pith with “nests” of sclereids, with a central secretory canal and a peripheral system of canals. Abietopitys sp. has a solid, lobed, homocelular pith associated with a secondary xylem containing cross-fields with non-contiguous pits (abietoid vascular rays). Xeromorphic features observed in these specimens, such as a pith with groups of sclereids and false growth rings, may be related to the depositional environment of the Irati Formation. The specific factor triggering the xeromorphy has not yet been determined, but the present study corroborates clearly the sedimentary evidence of dry conditions during the deposition of the Irati Formation, as seen in the 30-m-thick carbonate layer extending through four South American countries.
In this paper we present experimental and numerical studies of the electrohydrodynamic stretching of a sub-millimetre-sized salt water drop, immersed in oil with added non-ionic surfactant, and subjected to a suddenly applied electric field of magnitude approaching 1 kV/mm. By varying the drop size, electric field strength and surfactant concentration we cover the whole range of electric capillary numbers ( ) from 0 up to the limit of drop disintegration. The results are compared with the analytical result by Taylor (1964) which predicts the asymptotic deformation as a function of . We find that the addition of surfactant damps the transient oscillations and that the drops may be stretched slightly beyond the stability limit found by Taylor. We proceed to study the damping of the oscillations, and show that increasing the surfactant concentration has a dual effect of first increasing the damping at low concentrations, and then increasing the asymptotic deformation at higher concentrations. We explain this by comparing the Marangoni forces and the interfacial tension as the drops deform. Finally, we have observed in the experiments a significant hysteresis effect when drops in oil with large concentration of surfactant are subjected to repeated deformations with increasing electric field strengths. This effect is not attributable to the flow nor the interfacial surfactant transport.
The flow-induced vibration of bluff bodies is an important problem of many marine, civil, or mechanical engineers. In the design phase of such structures, it is vital to obtain good predictions of the fluid forces acting on the structure. Current methods rely on computational fluid dynamic simulations (CFD), with a too high computational cost to be effectively used in the design phase or for control applications. Alternative methods use heuristic mathematical models of the fluid forces, but these lack the accuracy (they often assume the system to be linear) or flexibility to be useful over a wide operating range. In this work we show that it is possible to build an accurate, flexible and low-computational-cost mathematical model using nonlinear system identification techniques. This model is data driven: it is trained over a user-defined region of interest using data obtained from experiments or simulations, or both. Here we use a Van der Pol oscillator as well as CFD simulations of an oscillating circular cylinder to generate the training data. Then a discrete-time polynomial nonlinear state-space model is fit to the data. This model relates the oscillation of the cylinder to the force that the fluid exerts on the cylinder. The model is finally validated over a wide range of oscillation frequencies and amplitudes, both inside and outside the so-called lock-in region. We show that forces simulated by the model are in good agreement with the data obtained from CFD.
The current study presents an effective framework for automated multi-objective optimization (MOO) of machining processes by using finite element (FE) simulations. The framework is demonstrated by optimizing a metal cutting process in turning AISI-1045, using an uncoated K10 tungsten carbide tool. The aim of the MOO is to minimize tool-chip interface temperature and tool wear depth, that are extracted from FE simulations, while maximizing the material removal rate. The effect of tool geometry parameters, i.e., clearance angle, rake angle, and cutting edge radius, and process parameters, i.e., cutting speed and feed rate on the objective functions are explored. Strength Pareto Evolutionary Algorithm (SPEA2) is adopted for the study. The framework integrates and connects several modules to completely automate the entire MOO process. The capability of performing the MOO in parallel is also enabled by adopting the framework. Basically, automation and parallel computing, accounts for the practicality of MOO by using FE simulations. The trade-off solutions obtained by MOO are presented. A knowledge discovery study is carried out on the trade-off solutions. The non-dominated solutions are analyzed using a recently proposed data mining technique to gain a deeper understanding of the turning process.
With outstanding material removal ability and high finish quality, robotic belt grinding has great advantages in processing difficult-to-machine materials like nickel-based superalloys. Tool wear is a severe problem in such grinding processes; thus, detection of tool wear is critical to precision finishing of a surface profile. This work proposes a novel acoustic signal-based detection method that combines a random forest (RF) classifier and a multiple linear regression (MLR) model to detect different wear periods and evaluate the remaining grinding ability for robotic belt grinding of nickel-based superalloys. The correlation between grinding sound and belt conditions is established through experimental studies and signal analysis. Through mapping the acoustic features of grinding sound and conditions of grinding belts, the RF classifier and the MLR model are trained and applied in prediction of grinding belt conditions. The total prediction accuracy of RF classifier for distinguishing different wear periods is over 94%, and the mean absolute percentage error of MLR model for evaluating the grinding ability in accelerated wear period is less than 9%. The online detection method can be used as a basis for adaptive control of grinding parameters to achieve precision profile finishing.
Tool design plays an important role in the development of dissimilar friction stir welding. This paper presents a design study of the welding tool through investigating the effect of pin flute radius during the friction stir welding of dissimilar AA7075-T651 and AA2024-T351 aluminum alloys. Five pin tools with different flute radii (0, 2, 3, 6, and a mm) were inspected under 900 rpm spindle speed and 150-mm/min traverse rate, taking the position of base materials on the advancing and retreating sides of weld into account. The metallographic analysis and tensile properties of the welding joints were presented and discussed. The results clarified that the flute radius affects the material flow pattern and weld quality. The strongest joint was produced using the welding pin tool with a flute of radius equals to that of the pin, regardless of the relative materials position.
This work presents new research results from double-electrode gas tungsten arc welding, a process variant that was developed with the aim of improving productivity and welding speed. A welding torch specially constructed for research was used with total current in the range of 200–600 A. Tests using a high-speed camera were conducted in order to characterize the arc morphology at different distances between the electrodes. It could be seen that the morphology of the arc and the voltage drop in each electrode change significantly with the increase in the welding speed, especially when there is a larger clearance distance between electrodes. The resulting arc presented bigger asymmetry as the welding speed was increased. Bead-on-plate tests were performed in order to evaluate their susceptibility to humping defects. The results showed that in comparison with the conventional GTAW, the double-electrode process enables a considerable increase in the maximum speed, without defects—taking into consideration the total applied current. This shows that this new process has great potential for expanding the range of gas tungsten arc welding in high-productivity welding applications.
Experimental correlation between varying processing parameter and compressive strength of polymer-modified Cement Mortar Composites was investigated. Polymer-modified Raffia fiber (RPF) cement mortar was developed by varying the RPF from 0 to 25%. Polyester and polyvinyl alcohol (PVA) were used in the production of the cement mortar. The curing time of 28 days were used. Compressive strength of the developed composite was examined. Factorial design of the experiment can be successfully employed to describe compressive strength of the cement mortar. The results show that increasing the volume percent of RPF from 0 to 25 vol% decreased the compressive strength compressive strength by (- 3.55(PVA), - 3.815(polyester)). Polymer-modified cement mortar has a positive effect on the compressive strength by (2.12(PVA), 1.22(polyester)). The curing days appear to be the most important variable with main effect of (5.043(PVA), 7.535(polyester)). The developed linear equation model can be used in predicting the compressive strength of the composites within the selected experimental conditions.
While several protocols are proposed in performing sub aquatic communication, routing and issues related to efficiency of energy are considered as important for the underwater sensor network. With a view of overcoming those issues, researches related to underwater sensor network happen to be still analyzing about how to improve performance of routing. In this study, we propose one new multi-layered routing protocol (MRP) that can be used in discovery of the efficient path and it also enhances the overall functioning of the end-to-end delay ration, effective utilization of energy, and network lifetime. Through advancing of its request for route from some node to a different one till reaching a particular surface node, MRP detects the path. The surface position nodes get stationed over the underwater and they are helpful to gather information which is sent from sub aquatic sensor nodes. We also suggest a new system called splice method that can be used in sub aquatic sensor correspondence for efficient data transmission via connection of the shortest route nodes. The splice function detects the sum of energy pertaining to a linked node and also the route having the greatest energy are being taken to forward data to surface position node. In case s surface position node happens to be busy with communication, then it will give an update instruction to the succeeding sub aquatic neighbor nor for taking a substitute surface position node for avoiding loss of data. After that, the sub aquatic node will take a substitute surface position node and will advance the particular information on to heap node. At the end, the surface position node advances the information on to the heap node; data gets transmitted in the shortest route in an energy-efficient manner and least possible distance with the lowest hops and ultimately, data gets forwarded by heap node to be stored on the Shell. Moreover, a list having information log is maintained by heap node, containing data about which data gets transmitted from which particular node and at what frequency of time duration the information was received, prior to storing the same in the shell. The particular shell that acts as buffer stores the data that has been advanced through heap node. In addition, we are also making use of segmented data reliable transfer protocol that achieves a dependable energy-effective transmission of data in sub aquatic sensor network.