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.     

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.     

Improving the performance of passive network monitoring applications with memory locality enhancements

Passive network monitoring is the basis for a multitude of systems that support the robust, efficient, and secure operation of modern computer networks. Emerging network monitoring applications are more demanding in terms of memory and CPU resources due to the increasingly complex analysis operations that are performed on the inspected traffic. At the same time, as the traffic throughput in modern network links increases, the CPU time that can be devoted for processing each network packet decreases. This leads to a growing demand for more efficient passive network monitoring systems in which runtime performance becomes a critical issue.In this paper we present locality buffering, a novel approach for improving the runtime performance of a large class of CPU and memory intensive passive monitoring applications, such as intrusion detection systems, traffic characterization applications, and NetFlow export probes. Using locality buffering, captured packets are being reordered by clustering packets with the same port number before they are delivered to the monitoring application. This results in improved code and data locality, and consequently, in an overall increase in the packet processing throughput and decrease in the packet loss rate. We have implemented locality buffering within the widely used libpcap packet capturing library, which allows existing monitoring applications to transparently benefit from the reordered packet stream without modifications. Our experimental evaluation shows that locality buffering improves significantly the performance of popular applications, such as the Snort IDS, which exhibits a 21% increase in the packet processing throughput and is able to handle 67% higher traffic rates without dropping any packets.     

On the gradient elastic wave propagation in cylindrical waveguides with microstructure

The present work describes the development of a complete theoretical framework of wave propagation in cylindrical waveguides possessing microstructure. In parallel, a thorough investigation of the full 3-D model of wave propagation in cylinders is presented. The first step is the spectral decomposition of the boundary value problem emerging via wave propagation analysis. The spectral representation of the specific gradient elasticity problem reflects the ability to construct all the possible propagating modes in cylindrical geometry. Several byproducts arise along the present work, which constitute generalizations of well known important features of classical elasticity and are indispensable for modeling the gradient elasticity problem. We note the construction of the set of dyadic Navier eigenfunctions which constitute the generalization of the Navier eigenvectors. The restriction of the Navier eigendyadics on cylindrical surfaces gives birth to the dyadic cylindrical harmonics, which constitute the generalization of the well known vector harmonics. This set is also a basis in the sense that the trace of every dyadic field on a cylindrical surface can be represented as a countable superposition of dyadic cylindrical harmonics. The method aims at providing the necessary theoretical establishment for the determination of the dispersion curves emerging in cortical bones.     

Virtual museums for all: employing game technology for edutainment

Museums have started to realise the potential of new technologies for the development of edutainment content and services for their visitors. Virtual reality technologies promise to offer a vivid, enjoyable experience to the museums guests, but the cost in time, effort and resources can prove to be overwhelming. In this paper, we propose the use of 3D game technologies for the purpose of developing affordable, easy to use and pleasing virtual environments. To this end, we present a case study based on an already developed version of a virtual museum and a newly implemented version that uses game technologies. The informal assessment indicates that game technologies can offer a prominent and viable solution to the need for affordable desktop virtual reality systems.     

An overview of 21 global and 43 regional land-cover mapping products

Land-cover (LC) products, especially at the regional and global scales, comprise essential data for a wide range of environmental studies affecting biodiversity, climate, and human health. This review builds on previous compartmentalized efforts by summarizing 23 global and 41 regional LC products. Characteristics related to spatial resolution, overall accuracy, time of data acquisition, sensor used, classification scheme and method, support for LC change detection, download location, and key corresponding references are provided. Operational limitations and uncertainties are discussed, mostly as a result of different original modelling outcomes. Upcoming products are presented and future prospects towards increasing usability of different LC products are offered. Despite the common realization of product usage by non-experts, the remote-sensing community has not fully addressed the challenge. Algorithmic development for the effective representation of inherent product limitations to facilitate proper usage by non-experts is necessary. Further emphasis should be placed on international coordination and harmonization initiatives for compatible LC product generation. We expect the applicability of current and future LC products to increase, especially as our environmental understanding increases through multi-temporal studies.     

A super-resolution mapping method using local indicator variograms

Super-resolution mapping (SRM) is a recently developed research task in the field of remotely sensed information processing. It provides the ability to obtain land-cover maps at a finer scale using relatively low-resolution images. Existing algorithms based on indicator geostatistics and downscaling cokriging offer an SRM approach using spatial structure models derived from real data. In this article, a novel SRM method is developed based on a sequentially produced with local indicator variogram (SLIV) SRM model. In the SLIV method, indicator variograms extracted from target-resolution classification are produced from a representative local area as opposed to using the entire image. This simplifies the application of the method since limited target-resolution reference data are required. Our investigation on three diverse case studies shows that the local window (approximately 2% of the entire study area) selection process offers comparable accuracy results to those using globally derived spatial structures, indicating our methodology to be a promising practice. Furthermore, comparison of the proposed method with random realizations indicates an improvement of 7–12% in terms of overall accuracy and 15–18% in terms of the kappa coefficient. The evaluation focused on a 270–30 m pixel size reconstruction as a potential popular application, for example moving from Moderate Resolution Imaging Spectroradiometer (MODIS) to Landsat-type resolutions.