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.     

Wireless fingerprinting indoor positioning using affinity propagation clustering methods

According to rapid extension of wireless sensor network localization, indoor localization using fingerprint has turned out to be more considerable lately. It contains of a database called Receive Strength Signal Indicator vectors, which is a primitive amount in wireless sensor network fingerprinting positioning. The equivalence of a few strategies is brought up from the literary works, and some new variants are presented in this study. A combination of a clustering strategy named affinity propagation and statistical and probabilistic positioning procedures is considered in this review and at the same time, the impact of some components in our methodology onto positioning precision will be investigated. Affinity propagation clustering method set up a common baseline for assessing the relative accuracy of various indoor location methods effectively. Eventually two coarse localization methods as Mahalanobis norm method and similarity to exemplar receive strength signal vector are compared based on positioning accuracy and performance. Experimental outcomes prove that the intended algorithm will advance the accuracy and localization error compared with the method without clustering.     

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.     

Reliable yet flexible software through formal model transformation (rule definition)

The results of literature review show that the need for both reliability and flexibility is increasingly becoming important among the various classes of software applications. Developing reliable yet flexible software is a hard problem. Although modeling methods enjoy a lot of advantages, the use of just one of them, in many cases, may not guarantee the development of reliable and flexible software. Formal modeling methods ensure reliability. However, lack of knowledge and high cost practically force developers to use semi-formal methods instead. Semi-formal (visual) modeling methods, which are widely used in practical large-scale software development, are not good enough for reliable software development. This paper proposes a new practical approach to the development of reliable yet flexible software. In the proposed approach, formal (Object-Z) and semi-formal (UML) models are transformed into each other using a set of bidirectional formal rules. Formal modeling and refinement ensure the reliability of software. Visual models facilitate the interactions among stakeholders who are not familiar enough with the complex mathematical concepts of formal methods. Visual models help detect the unexpected behavior and inconsistencies of software. Applying design patterns to visual models improves the flexibility of software. The transformation of formal and visual models into each other through the iterative and evolutionary process, proposed in this paper, helps develop the software applications that need to be highly reliable yet flexible. The feasibility of the proposed approach is evaluated using the multi-lift case study.     

A robust super-efficiency data envelopment analysis model for ranking of provincial gas companies in Iran

Conventional super-efficiency data envelopment analysis (DEA) models require the exact information of inputs or outputs. However, in many real world applications this simple assumption does not hold. Stochastic super-efficiency is one of recent methods which could handle uncertainty in data. Stochastic super-efficiency DEA models are normally formulated based on chance constraint programming. The method is used to estimate the efficiency of various decision making units (DMUs). In stochastic chance constraint super-efficiency DEA, the distinction of probability distribution function for input/output data is difficult and also, in several cases, there is not enough data for estimating of distribution function. We present a new method which incorporates the robust counterpart of super-efficiency DEA. The perturbation and uncertainty in data is assumed as ellipsoidal set and the robust super-efficiency DEA model is extended. The implementation of the proposed method of this paper is applied for ranking different gas companies in Iran.