A study on melt recycling of bio-based polypropylene/thermoplastic starch compound

The compulsion to use bioplastics has increased significantly today. One of the important aspects of plastics is their recyclability. Therefore, the important question of this research is that although bio-based compounds containing starch are sensitive to thermal-mechanical recycling processes, are such products thermally recyclable? To answer the question, polypropylene (PP)/thermoplastic starch (TPS) compound granules were extruded up to five times, and in the other part, single-extruded granules were blended at different ratios with virgin granules by extrusion. In order to characterize these samples, Fourier transform infrared spectroscopy, thermogravimetric analysis, differential scanning calorimetry, rotational disc rheometry, tensile properties, and appearance evaluation were used. The results showed that it is possible to recycle PP/TPS granules up to four times repetition of the extrusion operation and the fifth repetition also showed slight changes. There was also a blend of single-extruded granules with virgin material up to a 50:50% composition without significant variation.     

Adiabatic quantum optimization with qudits

Most realistic solid state devices considered as qubits are not true two-state systems. If the energy separation of the upper energy levels from the lowest two levels is not large, then these upper states may affect the evolution of the ground state over time and therefore cannot be neglected. In this work, we study the effect of energy levels beyond the lowest two energy levels on adiabatic quantum optimization in a device with a double-well potential as the basic logical element. We show that the extra levels can be modeled by adding additional ancilla qubits coupled to the original logical qubits, and that the presence of upper levels has no effect on the final ground state. We also study the influence of upper energy levels on the minimum gap for a set of 8-qubit spin glass instances.     

Electronic transport behavior of 4,6-bis(4-nitrophenyl)-2-phenyl-3,5-diaza-bicyclo[3.1.0]hex-2-ene (a bicyclic aziridine) as optical molecular switch: a first-principles approach

Journal of Computational Electronics - In this study, the electronic transport properties of 4,6-bis(4-nitrophenyl)-2-phenyl-3,5-diaza-bicyclo[3.1.0]hex-2-ene (as a bicyclic aziridine) have been...     

Fuzzy grey relational analysis for software effort estimation

Accurate and credible software effort estimation is a challenge for academic research and software industry. From many software effort estimation models in existence, Estimation by Analogy (EA) is still one of the preferred techniques by software engineering practitioners because it mimics the human problem solving approach. Accuracy of such a model depends on the characteristics of the dataset, which is subject to considerable uncertainty. The inherent uncertainty in software attribute measurement has significant impact on estimation accuracy because these attributes are measured based on human judgment and are often vague and imprecise. To overcome this challenge we propose a new formal EA model based on the integration of Fuzzy set theory with Grey Relational Analysis (GRA). Fuzzy set theory is employed to reduce uncertainty in distance measure between two tuples at the k ^th continuous feature ( | ( x_o(k) - x_i(k) | ) \left( {\left| {\left( {{x_o}(k) - {x_i}(k)} \right.} \right|} \right) .GRA is a problem solving method that is used to assess the similarity between two tuples with M features. Since some of these features are not necessary to be continuous and may have nominal and ordinal scale type, aggregating different forms of similarity measures will increase uncertainty in the similarity degree. Thus the GRA is mainly used to reduce uncertainty in the distance measure between two software projects for both continuous and categorical features. Both techniques are suitable when relationship between effort and other effort drivers is complex. Experimental results showed that using integration of GRA with FL produced credible estimates when compared with the results obtained using Case-Based Reasoning, Multiple Linear Regression and Artificial Neural Networks methods.     

Super-Streaming: A New Object Delivery Paradigm for Continuous Media Servers

A number of studies have focused on the design of continuous media, CM, (e.g., video and audio) servers to support the real-time delivery of CM objects. These systems have been deployed in local environments such as hotels, hospitals and cruise ships to support media-on-demand applications. They typically stream CM objects to the clients with the objective of minimizing the buffer space required at the client site. This objective can now be relaxed due to the availability of inexpensive storage devices at the client side. Therefore, we propose a Super-streaming paradigm that can utilize the client side resources in order to improve the utilization of the CM server. To support super-streaming, we propose a technique to enable the CM servers to deliver CM objects at a rate higher than their display bandwidth requirement. We also propose alternative admission control policies to downgrade super-streams in favor of regular streams when the resources are scarce. We demonstrate the superiority of our paradigm over streaming with both analytical and simulation models.Moreover, new distributed applications such as distant-learning, digital libraries, and home entertainment require the delivery of CM objects to geographically disbursed clients. For quality purposes, recently many studies proposed dedicated distributed architectures to support these types of applications. We extend our super-streaming paradigm to be applicable in such distributed architectures. We propose a sophisticated resource management policy to support super-streaming in the presence of multiple servers, network links and clients. Due to the complexity involved in modeling these architectures, we only evaluate the performance of super-streaming by a simulation study.     

Fluorescence in situ hybridization of three oncogenes on a leiomyoma

Using fluorescence in situ hybridization technique, expression of three oncogenes, C-myc, RARa, and cyclin-D was tested on a uterine leiomyoma. C-myc and RARa were amplified in approximately 30% and 90% of the cells, respectively. Numerous small signals of C-myc were indicative of the presence of double minutes. Amplification of RARa is being reported for the first time in a leiomyoma. Cyclin-D was normal in diploid cells while it was highly amplified in polyploid cells. Low levels of amplified C-myc and cyclin-D cells seem to be the reason for this tumor to be benign, while RARa could not be effective without the association of some other gene such as PML. Information presented here are significant toward developing new curative strategies such as gene-specific drugs and molecular manipulation to stop the activity of cancer gene. Further study may elucidate that how fibroids grow and maintain their rare benign nature.     

Stores clustering using a data mining approach for distributing automotive spare-parts to reduce transportation costs

Clustering of retail stores in a distribution network with specific geographical limits plays an important and effective role in distribution and transportation costs reduction. In this paper, the relevant data and information for an established automotive spare-parts distribution and after-sales services company (ISACO) for a 3-year period have been analyzed. With respect to the diversity and lot size of the available information such as stores location, order, goods, transportation vehicles and road and traffic information, three effecting factors with specific weights have been defined for the similarity function: 1. Euclidean distance, 2. Lot size 3. Order concurrency. Based on these three factors, the similarity function has been examined through 5 steps using the Association Rules principles, where the clustering of the stores is performed using k-means algorithm and similar stores are allocated to the clusters. These steps include: 1. Similarity function based on the Euclidean distances, 2. Similarity function based on the order concurrency, 3. Similarity function based on the combination of the order concurrency and lot size, 4. Similarity function based on the combination of these three factors and 5. Improved similarity function. The above mentioned clustering operation for each 5 cases addressed in data mining have been carried out using R software and the improved combinational function has been chosen as the optimal clustering function. Then, trend of each retail store have been analyzed using the improved combinational function and along with determining the priority of the depot center establishment for every cluster, the appropriate distribution policies have been formulated for every cluster. The obtained results of this study indicate a significant cost reduction (32%) in automotive spare-parts distribution and transportation costs.