Evaluating the influence of multiplexing schemes and buffer implementation on perceived VoIP conversation quality

This work presents a study of RTP multiplexing schemes, which are compared with the normal use of RTP, in terms of experienced quality. Bandwidth saving, latency and packet loss for different options are studied, and some tests of Voice over IP (VoIP) traffic are carried out in order to compare the quality obtained using different implementations of the router buffer. Voice quality is calculated using ITU R-factor, which is a widely accepted quality estimator. The tests show the bandwidth savings of multiplexing, and also the importance of packet size for certain buffers, as latency and packet loss may be affected. The customer’s experience improvement is measured, showing that the use of multiplexing can be interesting in some scenarios, like an enterprise with different offices connected via the Internet. The system is also tested using different numbers of samples per packet, and the distribution of the flows into different tunnels is found to be an important factor in order to achieve an optimal perceived quality for each kind of buffer. Grouping all the flows into a single tunnel will not always be the best solution, as the increase of the number of flows does not improve bandwidth efficiency indefinitely. If the buffer penalizes big packets, it will be better to group the flows into a number of tunnels. The router processing capacity has to be taken into account too, as the limit of packets per second it can manage must not be exceeded. The obtained results show that multiplexing is a good way to improve customer’s experience of VoIP in scenarios where many RTP flows share the same path.     

A multi-objective approach for the motion planning of redundant manipulators

Kinematic redundancy occurs when a manipulator possesses more degrees of freedom than those required to execute a given task. Several kinematic techniques for redundant manipulators control the gripper through the pseudo-inverse of the Jacobian, but lead to a kind of chaotic inner motion with unpredictable arm configurations. Such algorithms are not easy to adapt to optimization schemes and, moreover, often there are multiple optimization objectives that can conflict between them. Unlike single optimization, where one attempts to find the best solution, in multi-objective optimization there is no single solution that is optimum with respect to all indices. Therefore, trajectory planning of redundant robots remains an important area of research and more efficient optimization algorithms are needed. This paper presents a new technique to solve the inverse kinematics of redundant manipulators, using a multi-objective genetic algorithm. This scheme combines the closed-loop pseudo-inverse method with a multi-objective genetic algorithm to control the joint positions. Simulations for manipulators with three or four rotational joints, considering the optimization of two objectives in a workspace without and with obstacles are developed. The results reveal that it is possible to choose several solutions from the Pareto optimal front according to the importance of each individual objective.     

Evolutionary Generalized Radial Basis Function neural networks for improving prediction accuracy in gene classification using feature selection

Radial Basis Function Neural Networks (RBFNNs) have been successfully employed in several function approximation and pattern recognition problems. The use of different RBFs in RBFNN has been reported in the literature and here the study centres on the use of the Generalized Radial Basis Function Neural Networks (GRBFNNs). An interesting property of the GRBF is that it can continuously and smoothly reproduce different RBFs by changing a real parameter τ. In addition, the mixed use of different RBF shapes in only one RBFNN is allowed. Generalized Radial Basis Function (GRBF) is based on Generalized Gaussian Distribution (GGD), which adds a shape parameter, τ, to standard Gaussian Distribution. Moreover, this paper describes a hybrid approach, Hybrid Algorithm (HA), which combines evolutionary and gradient-based learning methods to estimate the architecture, weights and node topology of GRBFNN classifiers. The feasibility and benefits of the approach are demonstrated by means of six gene microarray classification problems taken from bioinformatic and biomedical domains. Three filters were applied: Fast Correlation-Based Filter (FCBF), Best Incremental Ranked Subset (BIRS), and Best Agglomerative Ranked Subset (BARS); this was done in order to identify salient expression genes from among the thousands of genes in microarray data that can directly contribute to determining the class membership of each pattern. After different gene subsets were obtained, the proposed methodology was performed using the selected gene subsets as new input variables. The results confirm that the GRBFNN classifier leads to a promising improvement in accuracy.     

Solving differential equations with Fourier series and Evolution Strategies

A novel mesh-free approach for solving differential equations based on Evolution Strategies (ESs) is presented. Any structure is assumed in the equations making the process general and suitable for linear and nonlinear ordinary and partial differential equations (ODEs and PDEs), as well as systems of ordinary differential equations (SODEs). Candidate solutions are expressed as partial sums of Fourier series. Taking advantage of the decreasing absolute value of the harmonic coefficients with the harmonic order, several ES steps are performed. Harmonic coefficients are taken into account one by one starting with the lower order ones. Experimental results are reported on several problems extracted from the literature to illustrate the potential of the proposed approach. Two cases (an initial value problem and a boundary condition problem) have been solved using numerical methods and a quantitative comparative is performed. In terms of accuracy and storing requirements the proposed approach outperforms the numerical algorithm.     

Searching in peer-to-peer networks

As peer-to-peer networks are proving capable of handling huge volumes of data, the need for effective search tools is lasting and imperative. During the last years, a number of research studies have been published, which attempt to address the problem of search in large, decentralized networks. In this article, we mainly focus on content and concept-based retrieval. After providing a useful discussion on terminology, we introduce a representative sample of such studies and categorize them according to basic functional and non-functional characteristics. Following our analysis and discussion we conclude that future work should focus on information filtering, re-ranking and merging of results, relevance feedback and content replication as well as on related user-centric aspects of the problem.     

Changes in free amino acids content in albacore (Thunnus alalunga) muscle during thermal processing

Summary The effects of cooking and sterilization at several temperatures on the free amino acids (FAA) content in albacore (Thunnus alalunga) muscle were studied during the processing of canned tuna. FAAs were derivatized witho-phtalaldehyde, separated on a C18 column by HPLC and detected by both fluorescence and ultra-violet detectors. After cooking the loss of FAAs was not significant. However, in the final product sterilized at 115 °C and 110 °C (throughout the whole process) there were significant losses with regard to the start material, but not at 118 °C (all temperatures leading to the same lethal F-value). The influence of the thermal process time at 115 °C was evaluated for 60 and 100 min. Significant losses were found between both canned products (25%) and between the raw fish and the final product (12% and 34%, process time 60 and 100 min, respectively). The determination of the content of FAA present in canned albacore may be a useful indication of the severity of the thermal processing.

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Veränderung im Gehalt an freien Aminosäuren in Thunfischmuskel (Tunnus alalunga) während der Wärmebehandlung

Zusammenfassung Die Wirkung des Kochens und Sterilisierens bei verschiedenen Temperaturen auf den Gehalt an freien Aminosäuren (FAA) im Thunfischmuskel (Thunnus alalunga) wurde während der Herstellung von Thunfischkonserven untersucht. Die freien Aminosäuren wurden mito-Phtalaldehyd derivatisiert, auf einer C-18-Kolonne mit HPLC abgetrennt und durch Fluorescenz und UV-Detektoren nachgewiesen. Nach dem Kochen war der Verlust an FAA nicht signifikant, jedoch in dem bei 110° und 115 °C sterilisierten Endprodukt ergaben sich signifikante Verluste bezogen auf das Ausgangsmaterial, aber nicht auf das bei 118 °C erhitzte; alle Erhitzungstemperaturen führen zu demselben letalen F-Wert. Der Zeiteinfluß der Erhitzung bei 115 °C wurde bei 60 und 100 min bewertet. Signifikante Verluste sind bei beiden Dosenkonserven aufgetreten (25%) und zwischen diesen und dem Rohfisch ( 12% und 34%) bei einer Erhitzungszeit von 60 und 100 min). Die Bestimmung des FAA-Gehalts in Thunfischkonserven kann für den Nachweis der Einwandfreiheit des thermischen Prozesses sehr nützlich sein.

     

Interpretation of long-chain structure from dilute solution properties of ultrahigh molecular weight polymers

Summary Systematic studies on solution properties of ultrahigh molecular weight polymers showed the existence of some special features as compared to usual length polymers. The paper discusses the possible appearance of branched structures in the polymerization process, structures able to influence the mentioned features. From experimental data on poly(methyl methacrylate), poly(butyl methacrylate) and polyacrylonitrile it appears that branching can be excluded, and the very high molecular weight domain considered may be responsible for the modifications observed in the molecular weight dependences on 2>, A₂ or [].     

Electrospun fibers based on porcine plasma: a rheological and morphological study

Iranian Polymer Journal - The present work focuses on the assessment of the ability of porcine plasma protein (PPP) to be electrospun satisfactorily to form fibre mats, and their rheological and...     

Genetic Insight into the Domain Structure and Functions of Dicer-Type Ribonucleases

Ribonuclease Dicer belongs to the family of RNase III endoribonucleases, the enzymes that specifically hydrolyze phosphodiester bonds found in double-stranded regions of RNAs. Dicer enzymes are mostly known for their essential role in the biogenesis of small regulatory RNAs. A typical Dicer-type RNase consists of a helicase domain, a domain of unknown function (DUF283), a PAZ (Piwi-Argonaute-Zwille) domain, two RNase III domains, and a double-stranded RNA binding domain; however, the domain composition of Dicers varies among species. Dicer and its homologues developed only in eukaryotes; nevertheless, the two enzymatic domains of Dicer, helicase and RNase III, display high sequence similarity to their prokaryotic orthologs. Evolutionary studies indicate that a combination of the helicase and RNase III domains in a single protein is a eukaryotic signature and is supposed to be one of the critical events that triggered the consolidation of the eukaryotic RNA interference. In this review, we provide the genetic insight into the domain organization and structure of Dicer proteins found in vertebrate and invertebrate animals, plants and fungi. We also discuss, in the context of the individual domains, domain deletion variants and partner proteins, a variety of Dicers’ functions not only related to small RNA biogenesis pathways.     

Functionalized Reduced Graphene Oxide as a Versatile Tool for Cancer Therapy

Cancer is one of the deadliest diseases in human history with extremely poor prognosis. Although many traditional therapeutic modalities—such as surgery, chemotherapy, and radiation therapy—have proved to be successful in inhibiting the growth of tumor cells, their side effects may vastly limited the actual benefits and patient acceptance. In this context, a nanomedicine approach for cancer therapy using functionalized nanomaterial has been gaining ground recently. Considering the ability to carry various anticancer drugs and to act as a photothermal agent, the use of carbon-based nanomaterials for cancer therapy has advanced rapidly. Within those nanomaterials, reduced graphene oxide (rGO), a graphene family 2D carbon nanomaterial, emerged as a good candidate for cancer photothermal therapy due to its excellent photothermal conversion in the near infrared range, large specific surface area for drug loading, as well as functional groups for functionalization with molecules such as photosensitizers, siRNA, ligands, etc. By unique design, multifunctional nanosystems could be designed based on rGO, which are endowed with promising temperature/pH-dependent drug/gene delivery abilities for multimodal cancer therapy. This could be further augmented by additional advantages offered by functionalized rGO, such as high biocompatibility, targeted delivery, and enhanced photothermal effects. Herewith, we first provide an overview of the most effective reducing agents for rGO synthesis via chemical reduction. This was followed by in-depth review of application of functionalized rGO in different cancer treatment modalities such as chemotherapy, photothermal therapy and/or photodynamic therapy, gene therapy, chemotherapy/phototherapy, and photothermal/immunotherapy.