Impact strength and melt viscosity of bisphenol-a polycarbonate and styrene-maleic-anhydride copolymer blends

Mixtures of 90, 80, and 70 percent by weight bisphenol-A-polycarbonate (PC) and 10, 20, and 30 percent by weight styrene maleic anhydride (SMA) copolymer were melt-blended in a single screw extruder. Differential scanning calorimetry (DCS) and scanning electron microscopy (SEM) were used to determine the miscibility of the blends. The viscosity, as a function of shear rate and temperature, was measured by an Instron capillary viscometer. The notched impact strength as a function of temperature was measured by an Izod impact tester. The results of DSC showed two glass transition temperatures which merged slightly towards each other, indicating marginal miscibility of these blends. There was a decrease in viscosity as the fraction of SMA copolymer was increased. The most significant decrease occurred with the initial addition of SMA copolymer. The viscosity also decreased with increases in temperature. The impact strength of the blends was also dependent on SMA copolymer content. The blends showed six to ten times lower impact strengths at room temperature than the 100 percent polycarbonate. SEM analysis helped to determine the reason why the impact strength was lower for the blends. High magnification showed the presence of SMA copolymer inclusions dispersed throughout the PC matrix. These inclusions, which increased in size as SMA copolymer content was increased, acted as defects in the system.     

Learning Read-Constant Polynomials of Constant Degree Modulo Composites

Boolean functions that have constant degree polynomial representation over a fixed finite ring form a natural and strict subclass of the complexity class ACC⁰. They are also precisely the functions computable efficiently by programs over fixed and finite nilpotent groups. This class is not known to be learnable in any reasonable learning model. In this paper, we provide a deterministic polynomial time algorithm for learning Boolean functions represented by polynomials of constant degree over arbitrary finite rings from membership queries, with the additional constraint that each variable in the target polynomial appears in a constant number of monomials. Our algorithm extends to superconstant but low degree polynomials and still runs in quasipolynomial time.     

Thermal conductivity of high polymers—the influence of molecular weight

Some earlier observations on the influence of molecular weight, plasticizer, and irradiation on the thermal conductivity of polymers is reviewed. Also some new data on polystyrene and some new calculations based on the theory of Hansen and Ho are presented. These data show almost a two fold increase in the thermal conductivity of polystyrene as the molecular weight goes from 900 to 100,000. Data on the moleculer weight effect on thermal conductivity appear to be consistent with the theoretical calculations.     

Hand-adaptive user interface: improved gestural interaction in virtual reality

Most interactive user interfaces (UIs) for virtual reality (VR) applications are based on the traditional eye-centred UI design principle, which primarily considers the user’s visual searching efficiency and comfort, but the hand operation performance and ergonomics are relatively less considered. As a result, the hand interaction in VR is often criticized as being less efficient and precise. In this paper, the user’s arm movement features, such as the choice of the hand being used and hand interaction position, are hypothesized to influence the interaction results derived from a VR study. To verify this, we conducted a free hand target selection experiment with 24 participants. The results showed that (a) the hand choice had a significant effect on the target selection results: for a left hand interaction, the targets located in spaces to the left were selected more efficiently and accurately than those in spaces to the right; however, in a right hand interaction, the result was reversed, and (b) the free hand interactions at lower positions were more efficient and accurate than those at higher positions. Based on the above findings, this paper proposes a hand-adaptive UI technique to improve free hand interaction performance in VR. A comprehensive comparison between the hand-adaptive UI and traditional eye-centred UI was also conducted. It was shown that the hand-adaptive UI resulted in a higher interaction efficiency and a lower physical exertion and perceived task difficulty than the traditional UI.

     

Effect of different surface treatments on polypropylene composites reinforced with yerba mate fibers: Physical,mechanical, chemical,and morphological properties

This study presents the preparation of post-consumer polypropylene (r-PP) composites filled with 30 wt% yerba mate (YM) stick particles. To improve the fiber–matrix adhesion, three surface treatments were performed: alkaline treatment with sodium hydroxide (NaOH) and use of 3-aminopropyltriethoxysilane (APTS) and maleic anhydride graft polypropylene copolymer (PP-g-MA) as coupling agents. Mechanical properties including tensile, flexural, and impact resistance were determined, and chemical (Fourier transform infrared spectroscopy [FTIR]), physical (water absorption), and morphological analyses were performed. The main findings show that the treatments were efficient in improving the mechanical properties of the composites, with emphasis on the r-PP/YM30/APTS and r-PP/YM30/PP-g-MA composites, which proved to be superior in tensile, flexion and impact strength and absorption of water compared to the untreated composite. The morphological analysis showed a better interaction between the fiber and the polymeric matrix for the composites with YM/APTS and YM/PP-g-MA, which corroborates the results of tensile and flexural strength, as well as with the spectra of FTIR in which the chemical modification of the fibers is observed. However, the results show that these treatments are promising in obtaining composites with recycled matrix with better properties.     

Suspension system performance optimization with discrete design variables

Suspension systems on commercial vehicles have become an important feature meeting the requirements from costumers and legislation. The performance of the suspension system is often limited by available catalogue components. Additionally the suspension performance is restricted by the travel speed which highly influences the ride comfort. In this article a suspension system for an articulated dump truck is optimized in sense of reducing elapsed time for two specified duty cycles without violating a certain comfort threshold level. The comfort threshold level is here defined as a whole-body vibration level calculated by ISO 2631-1. A three-dimensional multibody dynamics simulation model is applied to evaluate the suspension performance. A non-gradient optimization routine is used to find the best possible combination of continuous and discrete design variables including the optimum operational speed without violating a set of side constraints. The result shows that the comfort level converges to the comfort threshold level. Thus it is shown that the operational speed and hence the operator input influences the ride comfort level. Three catalogue components are identified by the optimization routine together with a set of continuous design variables and two operational speeds one for each load case. Thus the work demonstrates handling of human factors in optimization of a mechanical system with discrete and continuous design variables.     

Combining MODIS and Landsat imagery to estimate and map boreal forest cover loss

Estimation of forest cover change is important for boreal forests, one of the most extensive forested biomes, due to its unique role in global timber stock, carbon sequestration and deposition, and high vulnerability to the effects of global climate change. We used time-series data from the MODerate Resolution Imaging Spectroradiometer (MODIS) to produce annual forest cover loss hotspot maps. These maps were used to assign all blocks (18.5 by 18.5 km) partitioning the boreal biome into strata of high, medium and low likelihood of forest cover loss. A stratified random sample of 118 blocks was interpreted for forest cover and forest cover loss using high spatial resolution Landsat imagery from 2000 and 2005. Area of forest cover gross loss from 2000 to 2005 within the boreal biome is estimated to be 1.63% (standard error 0.10%) of the total biome area, and represents a 4.02% reduction in year 2000 forest cover. The proportion of identified forest cover loss relative to regional forest area is much higher in North America than in Eurasia (5.63% to 3.00%). Of the total forest cover loss identified, 58.9% is attributable to wildfires. The MODIS pan-boreal change hotspot estimates reveal significant increases in forest cover loss due to wildfires in 2002 and 2003, with 2003 being the peak year of loss within the 5-year study period. Overall, the precision of the aggregate forest cover loss estimates derived from the Landsat data and the value of the MODIS-derived map displaying the spatial and temporal patterns of forest loss demonstrate the efficacy of this protocol for operational, cost-effective, and timely biome-wide monitoring of gross forest cover loss.     

Evaluating the species energy relationship with the newest measures of ecosystem energy: NDVI versus MODIS primary production

Ecosystem energy has been shown to be a strong correlate with biological diversity at continental scales. Early efforts to characterize this association used the normalized difference vegetation index (NDVI) to represent ecosystem energy. While this spectral vegetation index covaries with measures of ecosystem energy such as net primary production, the covariation is known to degrade in areas of very low vegetation or in areas of dense forest. Two of the new vegetation products from the MODIS sensor, derived by integrating spectral reflectance, climate data, and land cover, are thought to better approximate primary productivity than NDVI. In this study, we determine if the new MODIS derived measures of primary production, gross primary productivity (GPP) and net primary productivity (NPP) better explain variation in bird richness than historically used NDVI. Moreover, we evaluate if the two productivity measures covary more strongly with bird diversity in those vegetation conditions where limitations of NDVI are well recognized.Biodiversity was represented as native landbird species richness derived from the North American Breeding Bird Survey. Analyses included correlation analyses among predictor variables, and univariate regression analyses between each predictor variable and bird species richness. Analyses were done at two levels: for all BBS routes across natural landscapes in North America; and for routes in 10 vegetation classes stratified by vegetated cover along a gradient from bare ground to herbaceous cover to tree cover. We found that NDVI, GPP and NPP were highly correlated and explained similar variation in bird species richness when analyzed for all samples across North America. However, when samples were stratified by vegetated cover, strength of correlation between NDVI and both productivity measures was low for samples with bare ground and for dense forest. The NDVI also explained substantially less variation in bird species richness than the primary production in areas with more bare ground and in areas of dense forest. We conclude that MODIS productivity measures have higher utility in studies of the relationship of species richness and productivity and that MODIS GPP and NPP improve on NDVI, especially for studies with large variation in vegetated cover and density.     

VISIM: Sequential simulation for linear inverse problems

Linear inverse Gaussian problems are traditionally solved using least squares-based inversion. The center of the posterior Gaussian probability distribution is often chosen as the solution to such problems, while the solution is in fact the posterior Gaussian probability distribution itself. We present an algorithm, based on direct sequential simulation, which can be used to efficiently draw samples of the posterior probability distribution for linear inverse problems. There is no Gaussian restriction on the distribution in the model parameter space, as inherent in traditional least squares-based algorithms.As data for linear inverse problems can be seen as weighed linear averages over some volume, block kriging can be used to perform both estimation (i.e. finding the center of the posterior Gaussian pdf) and simulation (drawing samples of the posterior Gaussian pdf). We present the kriging system which we use to implement a flexible GSLIB-based algorithm for solving linear inverse problems.We show how we implement such a simulation program conditioned to linear average data. The program is called VISIM as an acronym for Volume average Integration SIMulation. An effort has been made to make the program efficient, even for larger scale problems, and the computational efficiency and accuracy of the code is investigated.Using a synthetic cross-borehole tomography case study, we show how the program can be used to generate realizations of the a posteriori distributions (i.e. solutions) from a linear tomography problem. Both Gaussian and non-Gaussian a priori model parameter distributions are considered.