Empirical evaluation of H.264/SVC streaming in resource-constrained multihomed mobile networks

Nomadic users of streamed multimedia content in mobile networks are often faced with resource-constrained network paths that suffer from low bandwidth. Streaming high-quality video in such a challenging scenario demands a set of highly adaptive schemes, which have not been sufficiently explored in particular for the emerging H.264 Scalable Video Coding (H.264/SVC) standard. In this paper, we empirically investigate the performance of streaming H.264/SVC scalable video streams to users in multihomed mobile networks containing multiple available transmission paths. Previous work has demonstrated the feasibility of aggregating bandwidth of multiple paths to deliver video streams when no single, sufficiently high bandwidth path is available. We focus on evaluating the enhanced performance of multipath bandwidth-aggregation streaming by exploiting a quality-layers based, H.264/SVC-specific packet prioritisation scheme for quality-aware multipath packet scheduling and selective packet dropping in case of bandwidth shortage even after aggregation. Additionally, we explore a base-layer rate control scheme for H.264/SVC delivery in ultra-low bandwidth environments. Through extensive experimentation on a realistic hardware-based testbed, we obtain a comprehensive and insightful understanding of the behaviour of H.264/SVC streams when transmitted across multiple paths in mobile networks. We quantify the improvements offered by the use of H.264/SVC-specific packet prioritisation schemes compared with an existing generic scalable video prioritisation scheme, and the benefits by the use of base-layer rate control in ultra-low bandwidth situations. The performance of the multipath streaming schemes is further compared with that of an ideal single high bandwidth path. We also identify the remaining challenges that must be overcome if such streaming schemes are to offer performance close to that of the ideal single high bandwidth path.     

An empirical investigation of Technology Readiness among medical staff based in Greek hospitals

Technology readiness (TR) represents an individual’s mental readiness to accept new technologies. Although the TR scale has been used in many studies, its application in the healthcare context is limited. This paper focuses on identifying the TR profiles of medical staff and to model preference TR variations with respect to computer use, computer knowledge and computer feature demands. The study reports results from a nationwide study conducted in Greece, during a three-year period, which sampled responses from 604 physicians and nurses working in 14 Greek hospitals. Exploratory Structural Equation Modelling analysis is used in order to confirm the structure of the Technology Readiness Index. The results confirm the five groups of the TR taxonomy. Statistical differences were found between classes in information and communication technology (ICT) knowledge, ICT feature demands, hours of use per week as well as ICT use performance, but not in the general use of ICT. The results facilitate comprehension of the factors, which influence the use of ICT by medical staff and, in addition, they convey important policy and managerial implications. In conclusion, medical staff should be treated according to its TR taxonomy classes in order to expedite the acceptance and use of an ICT system.     

ON THE CHEMICAL OSCILLATIONS IN DIFFERENTIAL REACTORS: A CASE STUDY OF AMMONIA OXIDATION ON PLATINUM

Trying to identify the mechanism(s) responsible for isothermal oscillatory behavior of heterogeneous catalytic reaction systems, many investigators have assumed low conversions with respect to the limiting reactant(s) so that the bulk phase concentrations of the reactants may be considered constant. In this study, it is shown that even in a differential catalytic CSTR, in which conversion of the reactants may be less than %, the changes of the gas phase concentrations of the reacting.species may play a key role in the overall dynamic behavior of a reaction system; for instance, they may drive the oscillations observed. The reduction of nitric oxide by ammonia on polycrystalline platinum is discussed in detail within this context. Several other oxidation reactions are also discussed     

Parallel and scalable processing of spatio-temporal RDF queries using Spark

The ever-increasing size of data emanating from mobile devices and sensors, dictates the use of distributed systems for storing and querying these data. Typically, such data sources provide some spatio-temporal information, alongside other useful data. The RDF data model can be used to interlink and exchange data originating from heterogeneous sources in a uniform manner. For example, consider the case where vessels report their spatio-temporal position, on a regular basis, by using various surveillance systems. In this scenario, a user might be interested to know which vessels were moving in a specific area for a given temporal range. In this paper, we address the problem of efficiently storing and querying spatio-temporal RDF data in parallel. We specifically study the case of SPARQL queries with spatio-temporal constraints, by proposing the DiStRDF system, which is comprised of a Storage and a Processing Layer. The DiStRDF Storage Layer is responsible for efficiently storing large amount of historical spatio-temporal RDF data of moving objects. On top of it, we devise our DiStRDF Processing Layer, which parses a SPARQL query and produces corresponding logical and physical execution plans. We use Spark, a well-known distributed in-memory processing framework, as the underlying processing engine. Our experimental evaluation, on real data from both aviation and maritime domains, demonstrates the efficiency of our DiStRDF system, when using various spatio-temporal range constraints.

     

A general framework and optimization models for the scheduling of continuous chemical processes

We present a framework for the formulation and solution of continuous process scheduling problems. We focus on modeling transient operations such as startups, shutdowns, and transitions between steady states. First, we show how the concept of processing tasks can be generalized to represent continuous processes, including their transient operations. Second, we discuss how to systematically calculate the parameters describing material consumption/production and utility consumption during transient operations. Finally, we present new mixed-integer linear programming formulations for the scheduling of continuous chemical production.     

Superparamagnetic polyvinylpyrrolidone/chitosan/Fe3O4 electrospun nanofibers as effective U(VI) adsorbents

Magnetoactive electrospun fibrous membranes consisting of polyvinylpyrrolidone (PVP), chitosan (CS) and pre-fabricated, double-layer oleic acid-coated magnetite nanoparticles (OA.OA.Fe₃O₄) were fabricated and evaluated as new adsorbent materials for the removal and recovery of uranium (U(VI)) from aqueous solutions. The adsorption has been investigated by batch-type experiments and the solid material has been characterized by X-ray diffraction spectroscopy (XRD), Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), transmission electron microscopy/energy dispersive X-ray analysis (TEM/EDX) and vibration sample magnetometry (VSM) measurements prior and after uranium adsorption. The experimental adsorption data were found to be well fitted with the Langmuir isotherm and the pseudo-second order kinetic model. The results indicate that PVP/CS/OA.OA.Fe₃O₄ fibrous adsorbents exhibit good adsorption properties towards U(VI) in aqueous solutions, achieving a q_max value of 0.77 mol kg⁻¹ (183.3 mg g⁻¹) at pH 6.0. The experiments regarding the regeneration and reuse of the magnetoactive adsorbents were carried out using Na₂CO₃, at pH ~11. After four cycles, the percentage relative adsorption remained stable (~100%) whereas the desorption percentage decreased from 31.9% to 21.0%. Generally, the presented results demonstrate that the incorporation of the Fe₃O₄ NPs has a positive effect on the adsorption efficiency of U(VI) from aquatic environments.     

Enforcing Efficient Equilibria in Network Design Games via Subsidies

The efficient design of networks has been an important engineering task that involves challenging combinatorial optimization problems. Typically, a network designer has to select among several alternatives which links to establish so that the resulting network satisfies a given set of connectivity requirements and the cost of establishing the network links is as low as possible. The Minimum Spanning Tree problem, which is well-understood, is a nice example.In this paper, we consider the natural scenario in which the connectivity requirements are posed by selfish users who have agreed to share the cost of the network to be established according to a well-defined rule. The design proposed by the network designer should now be consistent not only with the connectivity requirements but also with the selfishness of the users. Essentially, the users are players in a so-called network design game and the network designer has to propose a design that is an equilibrium for this game. As it is usually the case when selfishness comes into play, such equilibria may be suboptimal. In this paper, we consider the following question: can the network designer enforce particular designs as equilibria or guarantee that efficient designs are consistent with users’ selfishness by appropriately subsidizing some of the network links? In an attempt to understand this question, we formulate corresponding optimization problems and present positive and negative results.     

Rollout sampling approximate policy iteration

Several researchers have recently investigated the connection between reinforcement learning and classification. We are motivated by proposals of approximate policy iteration schemes without value functions, which focus on policy representation using classifiers and address policy learning as a supervised learning problem. This paper proposes variants of an improved policy iteration scheme which addresses the core sampling problem in evaluating a policy through simulation as a multi-armed bandit machine. The resulting algorithm offers comparable performance to the previous algorithm achieved, however, with significantly less computational effort. An order of magnitude improvement is demonstrated experimentally in two standard reinforcement learning domains: inverted pendulum and mountain-car.     

Design of output constraints for model-based non-square controllers using interval operability

A steady-state interval operability methodology is introduced here for multivariable non-square systems with fewer inputs than output variables to be used in the design of model-based constrained controllers (MPC, DMC). For such systems, set-point control is not possible for all the outputs and interval control is needed. The proposed iterative approach enables the selection of the needed interval constraints systematically, so that the tightest possible control is achieved without rendering the control problem infeasible. The application of this methodology to high-dimensional industrial problems characterizing processes of Air Products and Chemicals and DuPont shows that very significant reduction of the constrained region can be achieved from the steady-state point of view. Ratios of the initial to the calculated volume of the constrained regions examined range between 10⁴ and 10⁸.