Tensile behavior of glass fiber-reinforced acetal polymer

The tensile stress-strain behavior of glass fiber-reinforced polyacetal resin was investigated for various fiber concentrations, fiber length distributions, and finishing agents. The polyacetal fiber blends change considerably in strength and elongation at break when treated with ammonium chloride, but otherwise similar specimens still follow a common stress-strain curve to a point shortly before failure. As the mean fiber length decreases, the modulus and tensile strength fall, but the elongation at break remains almost unchanged. The observed tensile behavior is discussed in terms of a simplified model, which assigns the fibers to two categories: a fraction α parallel to the applied load, and the remainder distributed in a plane perpendicular to the load axis. By fitting this model to the stress-strain curves, two other constants of each system are derived: a length-dependent efficiency factor β for parallel fibers, whose magnitude agrees with the predictions of Rosen and his co-workers, and a factor γ which expresses the constraint of the matrix resin by the “transverse” fibers. The behavior of γ is consistent with Tsai's theory of the transverse modulus of laminates, if a reasonable amount of fiber–fiber contact is assumed. In terms of this model, possible interpretations of the behavior under repeated loading and the mechanism of tensile failure are presented.     

Spectral Ray Differentials

Light refracted by a dispersive interface leads to beautifully colored patterns that can be rendered faithfully with spectral Monte‐Carlo methods. Regrettably, results often suffer from chromatic noise or banding, requiring high sampling rates and large amounts of memory compared to renderers operating in some trichromatic color space. Addressing this issue, we introduce spectral ray differentials, which describe the change of light direction with respect to changes in the spectrum. In analogy with the classic ray and photon differentials, this information can be used for filtering in the spectral domain. Effectiveness of our approach is demonstrated by filtering for offline spectral light and path tracing as well as for an interactive GPU photon mapper based on splatting. Our results show considerably less chromatic noise and spatial aliasing while retaining good visual similarity to reference solutions with negligible overhead in the order of milliseconds.     

Surface properties of surfactants derived from natural products. Part 1: Syntheses and structure/property relationships—Solubility and emulsification

Several novel and some previously known, mostly sugar-based, surfactants have been synthesized and some of their surface properties have been characterized and compared with those of commercial nonylphenol ethoxylates. The surfactant solubility in water, ethanol, and dodecane was studied. The properties of these compounds as emulsification agents in systems composed of the surfactant with water/isopropyl myristate, water/rapeseed oil, and water/dodecane are presented. The aqueous solubility of the surfactants follows the general trend expected from their hydrophilic-lipophilic balance according to Griffin (HLBG), but it is also clear that the nature of the headgroup and the structure of the nonpolar part affect the solubility in a manner not aptured in the standard HLBG concept. An ester or amine group as the connecting unit between the hydrophile and the hydrophobe produces a more water-soluble surfactant than the corresponding amide derivative. Some effective emulsifiers were found. For instance, the surfactants with a dehydroabietic nonpolar group appear to be promosing emulsifiers. Most sugar-based surfactants were able to form macroemulsions of up to around 2 wt/vol% of oil. The stability of many of these amulsions was very high, extending for months.     

Thermal stability of composite phase change material microcapsules incorporated with silver nano-particles

This paper reports a study on the thermal stability of phase change material microcapsules that are incorporated with silver nano-particles (Ag-NPs). The novel microcapsules were fabricated by the technique of in situ polymerization, with aminoplast as the wall and phase change material bromo-hexadecane (PCM BrC16) as the core. Thermal gravimetry (TG) analysis was applied to measure the thermal stability of these microcapsules and surface morphology of the microcapsules was observed by means of scanning electron microscopy (SEM) after an application of curing treatment at 130 °C. Comparing with conventional phase change material microcapsules (PCMMs), nano-composite phase change material microcapsules (NCPCMMs) have higher thermal stability. This can be attributed to nano-composite structure of the microcapsules, in which metal Ag-NPs distributed on the surface to increase wall toughness and strength. The possible reinforcement mechanisms of the nano-composite structure are explored.     

Microstructure and ultimate properties of injection molded amorphous engineering plastics: Poly(ether imide) and poly(2,6-dimethyl-1,4-phenylene ether)

Specimens of two engineerig plastics i.e., poly(ether imide), PEI, and poly(2,6-dimethyl- 1,4-phenylene ether), PPE, were injection molded employing a 40t Van Dorn injection molding machine and industrial practices. The mold and melt temperatures and the injection speed were varied in a limited range which furnished acceptable samples. The density, birefringence, residual stress distributions, flexure and tensile properties, and crack development of the injection molded specimens were studied. Vacuum compression molded samples were also prepared to investigate the role played by the cooling rate in shaping microstructural distributions. The results revealed significant differences in the development of microstructure of the molded specimens of the two resins, which was related to rheology and molding conditions on one hand and to development of cracks and ultimate properties on the other hand.     

Variable temperature13C MAS NMR of KFe2Mn(CO)12 in the solid state and as aCatalyst precursor on a carbon support

Variable temperature¹³C MAS NMR spectra are reported for¹³CO-enriched KFe₂Mn(CO)₁₂ as a solid and also as dispersed clusters on a carbon support. The spectrum of KFe₂Mn(CO)₁₂ at 300 K agrees with the proposed structure for this cluster and shows that the cluster is static. Two bridging carbonyl resonances are clearly resolved and, by comparison with¹³C MAS NMR spectra of Mn₂(CO)₁₀ and Fe₂(CO)₉, all terminal resonances for the cluster can be assigned. When the cluster is supported on carbon, two broad resonances are observed at room temperature which are assignable to KFe₂Mn(CO)₁₂ and a decomposition product, Mn₂(CO)₁₀. The carbonyl ligands in both supported clusters are completely averaged, and KFe₂Mn(CO)₁₂ on the carbon surface demonstrates fluxional behavior similar to that observed for the cluster in solution. For this fluxional process, activation energies of 0.6 kcal/mol and 0.5 kcal/mol are estimated for carbon-supported KFe₂Mn(CO)₁₂ and Mn₂(CO)₁₀, respectively.     

The longitudinal impact of enterprise system users’ pre-adoption expectations and organizational support on post-adoption proficient usage

Although enterprise systems (ES) are ubiquitous, many firms report less than stellar payoffs from these costly investments, with underutilization often attributed to failures in the implementation process. Unfortunately, research has not provided sufficient insights into these failures, in part because it has focused on actual usage, as opposed to proficient usage, as the benchmark for successful implementation. Moreover, research has not generally examined how the adoption process unfolds over time, thus overlooking potential underlying mechanisms that may help explain how adopters achieve proficiency. To begin addressing these shortcomings, we study how adopters’ pre-adoption expectations, enacted over time, can influence their post-adoption proficiency, by shaping how and why they spend time using the system during the adoption period. We analyzed time-lagged survey data from 153 financial analysts, required to adopt new ES-based software, at a multinational bank. We found that adopters who hold pre-adoption expectations reflecting greater internal and external motives to adopt the system as well as systematically integrate it into their work routines are more apt to use the system in ways that enhance their cumulative knowledge of it, and subsequently develop higher levels of proficiency post-adoption. Moreover, greater organizational support enhanced the impact of adopters’ expectations on proficiency, except when their actual use is low in which case organizational support had an adverse effect.     

Measuring Cognitive Load Using Linguistic Features: Implications for Usability Evaluation and Adaptive Interaction Design

An intelligent adaptable system, aware of a user’s experienced cognitive load, may help improve performance in complex, time-critical situations by dynamically deploying more appropriate output strategies to reduce cognitive load. However, measuring a user’s cognitive load robustly, in real-time is not a trivial task. Many research studies have attempted to assess users’ cognitive load using different measurements, but these are often unsuitable for deployment in real-life applications due to high intrusiveness. Relatively novel linguistic behavioral features as potential indices of user’s cognitive load is proposed. These features may be collected implicitly and nonintrusively supporting real-time assessment of users’ cognitive load and accordingly allowing adaptive usability evaluation and interaction. Results from a laboratory experiment show significantly different linguistic patterns under different task complexities and cognitive load levels. Implications of the research for adaptive interaction are also discussed, that is, how the cognitive load measurement-based approach could be used for user interface evaluation and interaction design improvement.     

Approximate similarity search for online multimedia services on distributed CPU–GPU platforms

Similarity search in high-dimensional spaces is a pivotal operation for several database applications, including online content-based multimedia services. With the increasing popularity of multimedia applications, these services are facing new challenges regarding (1) the very large and growing volumes of data to be indexed/searched and (2) the necessity of reducing the response times as observed by end-users. In addition, the nature of the interactions between users and online services creates fluctuating query request rates throughout execution, which requires a similarity search engine to adapt to better use the computation platform and minimize response times. In this work, we address these challenges with Hypercurves, a flexible framework for answering approximate k-nearest neighbor (kNN) queries for very large multimedia databases. Hypercurves executes in hybrid CPU–GPU environments and is able to attain massive query-processing rates through the cooperative use of these devices. Hypercurves also changes its CPU–GPU task partitioning dynamically according to the observed load, aiming for optimal response times. In our empirical evaluation, dynamic task partitioning reduced query response times by approximately 50 % compared to the best static task partition. Due to a probabilistic proof of equivalence to the sequential kNN algorithm, the CPU–GPU execution of Hypercurves in distributed (multi-node) environments can be aggressively optimized, attaining superlinear scalability while still guaranteeing, with high probability, results at least as good as those from the sequential algorithm.