Thermo-oxidative degradation during sintering of polyethylene particles

Polymer sintering is not only a well-established procedure for producing functional polymeric parts, but it is also the basis for the relatively new additive manufacturing technique, selective laser sintering. Although studying the impact of thermo-oxidative degradation during sintering has significant practical importance, few studies have focused on this aspect of the sintering process. In the present work, we have investigated the active thermo-oxidative degradation mechanisms during sintering of high-density polyethylene (HDPE) particles, the conditions that promote them, and their respective impact on the morphological evolution of the polyethylene particles. To perform a comprehensive study, we have complemented the rheological, thermal, chemical, and morphological analysis of the sintered HDPE particles with the study of their ensemble pore structure. We observed two distinct degradation regimes. At the beginning, the relatively low concentration of oxygen in the particles led to the dominance of branching and resulted in a pore structure evolution limited to surface relaxation. In the second regime and with the diffusion of more oxygen, chain scission became the dominant route. In this regime, the emergence of highly mobile short chains markedly accelerated the pore evolution.     

Experimental and numerical evaluation of rigid column to baseplate connection under cyclic loading

One of the most important connections in steel structures is column to the baseplate connection. This kind of connection has a complex nature, due to different behaviors of the constituents, including baseplate, grout, foundation, and anchor bolts. Studying the impact of this connection in general behavior of structure seems to be an essential issue, especially during earthquake, which is more likely to be associated with plastic hinge forming. The main goal of this work is to provide a hybrid modeling for describing hysteresis behavior of column to the baseplate connection. Within hybrid modeling, a mechanical model is accomplished by using neural network model components. Our findings reveal that hybrid models are able to show pinched hysteresis complex behavior of baseplate connections. Also, the proposed hybrid developed model is robust and reliable approaches for predicting behaviors of a recent designed connection.     

VISCOMETRIC MEASUREMENT OF CHROMIUM(III)-POLYACRYLAMIDE GELS†

Gelled polymers are being used increasingly to modify the movement of injected fluids in secondary and enhaced oil recovery processes. A common gelation process involves the reduction of Cr(VI) to Cr(III) in the presence of polyacrylamide. The Cr(III) reacts or interacts with the polymer to form a gel network. Although correlations of gelation time with principal process variables have been obtained, viscometric data have not been reported during or after gelation. These data are needed for fluid flow calculations in surface equipment and estimation of flow behaviour in reservoir rocks.

A Weissenberg Rheogoniometer, with cone and plate geometry, was used to obtain viscometric data for the gelation of polyacrylamide and chromium (III). Solutions consisting of polyacrylamide polymer, sodium dichromate-dihydrate and sodium bisulfite were gelled under a steady shear field at constant temperature. The shear stress versus time profile for the galation process was interpreted to define a gelation time and to determine the apparent viscosity of the gelled fluid. The gelation time decreased as the applied shear rate increased up to about 14.25 sec^-1 and was affected by shear rate history. Viscometric properties of the gelled solutions were determined. Apparent viscosity of the gelled solutions decreased as the shear rate under which they were formed increased.

Post gelation studies indicated that gels exhibited a residual stress at zero shear rate and behaved as Bingham plastics under steady shear. Gels formed at low shear rates were more viscous than gels formed at high shear rates. However, the structure of these gels was susceptible to shear degradation.     

Data-dependent dissimilarity measure: an effective alternative to geometric distance measures

Nearest neighbor search is a core process in many data mining algorithms. Finding reliable closest matches of a test instance is still a challenging task as the effectiveness of many general-purpose distance measures such as \(\ell _p\)-norm decreases as the number of dimensions increases. Their performances vary significantly in different data distributions. This is mainly because they compute the distance between two instances solely based on their geometric positions in the feature space, and data distribution has no influence on the distance measure. This paper presents a simple data-dependent general-purpose dissimilarity measure called ‘\(m_p\)-dissimilarity’. Rather than relying on geometric distance, it measures the dissimilarity between two instances as a probability mass in a region that encloses the two instances in every dimension. It deems two instances in a sparse region to be more similar than two instances of equal inter-point geometric distance in a dense region. Our empirical results in k-NN classification and content-based multimedia information retrieval tasks show that the proposed \(m_p\)-dissimilarity measure produces better task-specific performance than existing widely used general-purpose distance measures such as \(\ell _p\)-norm and cosine distance across a wide range of moderate- to high-dimensional data sets with continuous only, discrete only, and mixed attributes.     

Dynamic bitrate allocation of interactive real-time streamed multi-view video with view-switch prediction

In multi-view video (MVV), the real-world scene is usually captured by more than two cameras positioned in an array. A viewer can consume MVV using either a non-interactive or an interactive transmission method. In the context of interactive MVV streaming, view switching may cause a long delay due to the frequent requests by the viewer. In this paper, we consider the use case of real-time interactive MVV (IMVV) streaming, where the view switching delay problem has a significant user experience impact. Our proposed method compress and send all the captured views using a dynamic bitrate allocation method. Also, a novel prediction algorithm has been used to choose possible views that the user might switch to. The predicted view switching is mapped to a hidden Markov model, and the transition probability is solved using Zipf distribution. The experimental results of the proposed method show a superior performance on an objective metric and view-switching delay for better viewing quality over the existing method.     

Usage and impact of technology enabled job learning

Individuals use technology to experiment with new ways of carrying out their tasks and in doing so they learn more about their jobs. The current study examines the role of technology enabled job learning as a key component in the complex relationship between information systems use and technology outcomes. Data from 308 end-users were analyzed to evaluate the relationships between system use and technology enabled job learning, and technology enabled job learning and technology outcomes. Technology enabled job learning was conceptualized in terms of how computer applications helped individuals learn and better perform their jobs. System use was conceptualized in terms of decision support, work integration, and customer service. Technology outcomes were conceptualized in terms of management control, task innovation, task productivity, and customer satisfaction. Results suggest that systems use has a significant, positive effect on job learning, and that job learning has a significant, positive effect on technology outcomes. Post hoc analyses were then conducted to examine the potential mediating role of job learning between systems use and technology outcomes. The findings from this research lead to a greater understanding of how patterns of systems use influence organizationally relevant outcomes through technology-enabled job learning.     

Artificial neural network to predict the effect of heat treatments on Vickers microhardness of low-carbon Nb microalloyed steels

In the present study, an artificial neural networks-based model (ANNs) was developed to predict the Vickers microhardness of low-carbon Nb microalloyed steels. Fourteen parameters affecting the Vickers microhardness were considered as inputs, including the austenitizing temperature, cooling rate, initial austenite grain size, different chemical compositions and Nb in solution. The network was then trained to predict the Vickers microhardness amounts as outputs. A Multilayer feed-forward back-propagation network was developed and trained using experimental data form literatures. Five low-carbon Nb microalloyed steels and one low-carbon steel without Nb were investigated. The effects of austenitizing temperature (900–1,100°C) and subsequent cooling rate (0.15–227°C/s) and initial austenite grain size (5–130 μm) on the Vickers microhardness of steels were modeled by ANNs as well. The predicted values are in very good agreement with the measured ones, indicating that the developed model is very accurate and has the great ability for predicting the Vickers microhardness.