Observations on the estimation of scavenging activity of phenolic compounds using rapid 1,1-diphenyl-2-picrylhydrazyl (DPPH<Superscript>•</Superscript>) tests

Rapid 1,1-diphenyl-2-picrylhydrazyl (DPPH^•) tests are often applied to classify the scavenging activity of phenolic compounds (AH). Published analytical protocols differ in more than one experimental condition, and results for the relative order or magnitude of activity are often contradictory. In this work, parameters such as duration of test, [AH]/[DPPH^•] molar ratio, and solvent effects were examined and discussed. The test duration and the value of the [AH]/[DPPH^•] ratio did not influence the order of activity among tested antioxidants. Ethanol, commonly used as solvent in such tests, was compared with acetonitrile and tert-butyl alcohol. Solvent properties such as the ability to form hydrogen bonds with the AH seem to influence the level of the relative activity (%RSA). Higher %RSA values were observed in ethanol. The activity of the most polar compounds was affected the most, and in some cases (caffeic, dihydrocaffeic, and rosmarinic acids) the order of activity was changed owing to different kinetics. Standardization of the analytical protocol should include a 20-min reaction period and a molar ratio that permits attainment of a 60–80% RSA value for the most potent antioxidant. Solvent choice is critical for classifying activity. Safe classification can be based only on results from kinetic studies.     

Hand dexterity assessment based on mouse pointer trajectory measurements in children with learning disabilities

Universal Access in the Information Society - We present an investigation of the systematic differences in upper limb motor skills between children with learning disabilities (LDs) and children...     

Worker Exposure Monitoring of Suspended Particles in a Thermal Spray Industry

The purpose of the present work was the investigation and characterization of the quality of air in a thermal spray industry, in Greece. The activities that take place in the specific facility, as well as in most other similar industries, include thermal spraying and several mechanical and metallurgical tasks that generate airborne particles, such as grit-blasting, cutting and grinding of metallic components. Since the main focus of this work was the workers exposure to airborne particles and heavy metals, portable air samplers with quartz fiber filters, were used daily for 8 h. Three samplers, carried from different employees, were used for a period of 1 month. Results showed that both particles and heavy metals concentrations were low, even in the production site, which was the most susceptible area. The only exceptions were observed in the case of cleaning and maintenance activities in the thermal spray booth and in the case of spraying outside the booth. The main reason for the low concentrations is the fact that most of the activities that could produce high-particle concentrations are conducted in closed, well-ventilated systems. Statistical elaboration of results showed that particles are correlated with Ni, Cu, Co. The same conclusion is extracted for Fe, Mn. These correlations indicate possible common sources.     

Approximate variational inference based on a finite sample of Gaussian latent variables

Variational methods are employed in situations where exact Bayesian inference becomes intractable due to the difficulty in performing certain integrals. Typically, variational methods postulate a tractable posterior and formulate a lower bound on the desired integral to be approximated, e.g. marginal likelihood. The lower bound is then optimised with respect to its free parameters, the so-called variational parameters. However, this is not always possible as for certain integrals it is very challenging (or tedious) to come up with a suitable lower bound. Here, we propose a simple scheme that overcomes some of the awkward cases where the usual variational treatment becomes difficult. The scheme relies on a rewriting of the lower bound on the model log-likelihood. We demonstrate the proposed scheme on a number of synthetic and real examples, as well as on a real geophysical model for which the standard variational approaches are inapplicable.     

Dynamic disturbance rejection controllers for neutral time delay systems with application to a central heating system

In the present paper the problem of disturbance rejection of single input-single output neutral time delay systems with multiple measurable disturbances is solved via dynamic controllers. In particular, the general form of the controller matrices is presented, while the necessary and sufficient conditions for the controller to be realizable are offered. The proposed technique is applied to a test case neutral time delay central heating system. In particular, the nonlinear model of the plant and its linearized approximation are presented. Based on the linearized model, a two-stage controller is designed in order to regulate the room temperature and the boiler effluent temperature. The performance of the closed loop system is investigated through computational experiments.     

Segmentation of historical machine-printed documents using Adaptive Run Length Smoothing and skeleton segmentation paths

In this paper, we strive towards the development of efficient techniques in order to segment document pages resulting from the digitization of historical machine-printed sources. This kind of documents often suffer from low quality and local skew, several degradations due to the old printing matrix quality or ink diffusion, and exhibit complex and dense layout. To face these problems, we introduce the following innovative aspects: (i) use of a novel Adaptive Run Length Smoothing Algorithm (ARLSA) in order to face the problem of complex and dense document layout, (ii) detection of noisy areas and punctuation marks that are usual in historical machine-printed documents, (iii) detection of possible obstacles formed from background areas in order to separate neighboring text columns or text lines, and (iv) use of skeleton segmentation paths in order to isolate possible connected characters. Comparative experiments using several historical machine-printed documents prove the efficiency of the proposed technique.     

Spatially Varying Mixtures Incorporating Line Processes for Image Segmentation

Spatially varying mixture models are characterized by the dependence of their mixing proportions on location (contextual mixing proportions) and they have been widely used in image segmentation. In this work, Gauss-Markov random field (MRF) priors are employed along with spatially varying mixture models to ensure the preservation of region boundaries in image segmentation. To preserve region boundaries, two distinct models for a line process involved in the MRF prior are proposed. The first model considers edge preservation by imposing a Bernoulli prior on the normally distributed local differences of the contextual mixing proportions. It is a discrete line process model whose parameters are computed by variational inference. The second model imposes Gamma prior on the Student’s-t distributed local differences of the contextual mixing proportions. It is a continuous line process whose parameters are also automatically estimated by the Expectation-Maximization (EM) algorithm. The proposed models are numerically evaluated and two important issues in image segmentation by mixture models are also investigated and discussed: the constraints to be imposed on the contextual mixing proportions to be probability vectors and the MRF optimization strategy in the frameworks of the standard and variational EM algorithm.     

The MORPHEUS Heterogeneous Dynamically Reconfigurable Platform

Reconfigurable computing offers a wide range of low cost and efficient solutions for embedded systems. The proper choice of the reconfigurable device, the granularity of its processing elements and its memory architecture highly depend on the type of application and their data flow. Existing solutions either offer fine grain FPGAs, which rely on a hardware synthesis flow and offer the maximum degree of flexibility, or coarser grain solutions, which are usually more suitable for a particular type of data flow and applications. In this paper, we present the MORPHEUS architecture, a versatile reconfigurable heterogeneous System-on-Chip targeting streaming applications. The presented architecture exploits different reconfigurable technologies at several computation granularities that efficiently address the different applications needs. In order to efficiently exploit the presented architecture, we implemented a complete software solution to map C applications to the reconfigurable architecture. In this paper, we describe the complete toolset and provide concrete use cases of the architecture.     

Probabilistic model checking for the quantification of DoS security threats

Secure authentication features of communication and electronic commerce protocols involve computationally expensive and memory intensive cryptographic operations that have the potential to be turned into denial-of-service (DoS) exploits. Recent proposals attempt to improve DoS resistance by implementing a trade-off between the resources required for the potential victim(s) with the resources used by a prospective attacker. Such improvements have been proposed for the Internet Key Exchange (IKE), the Just Fast Keying (JFK) key agreement protocol and the Secure Sockets Layer (SSL/TLS) protocol. In present article, we introduce probabilistic model checking as an efficient tool-assisted approach for systematically quantifying DoS security threats. We model a security protocol with a fixed network topology using probabilistic specifications for the protocol participants. We attach into the protocol model, a probabilistic attacker model which performs DoS related actions with assigned cost values. The costs for the protocol participants and the attacker reflect the level of some resource expenditure (memory, processing capacity or communication bandwidth) for the associated actions. From the developed model we obtain a Discrete Time Markov Chain (DTMC) via property preserving discrete-time semantics. The DTMC model is verified using the PRISM model checker that produces probabilistic estimates for the analyzed DoS threat. In this way, it is possible to evaluate the level of resource expenditure for the attacker, beyond which the likelihood of widespread attack is reduced and subsequently to compare alternative design considerations for optimal resistance to the analyzed DoS threat. Our approach is validated through the analysis of the Host Identity Protocol (HIP). The HIP base-exchange is seen as a cryptographic key-exchange protocol with special features related to DoS protection. We analyze a serious DoS threat, for which we provide probabilistic estimates, as well as results for the associated attacker and participants' costs.