Nanostructured TiO2-based mixed metal oxides prepared using microemulsions for carbon monoxide detection

Nanostructured powders of Nb-doped TiO₂ (TN) and SnO₂ mixed with Nb-doped TiO₂ in two different atomic ratios—10 to 1 (TSN 101) and 1 to 1 (TSN 11)—were synthesized using the reverse micelle microemulsion of a nonionic surfactant (brine solution/1-hexanol/Triton X-100/cyclohexane). The powders were characterized by transmission electron microscopy (TEM) and X-ray diffraction (XRD). Thick films were fabricated for gas sensors and characterized by XRD analysis and field emission scanning electron microscopy (FE-SEM). The effects of the film morphology and firing temperature in the range 650–850 °C on CO sensitivity were studied. The best gas response, expressed as the ratio between the resistance in air and the resistance under gas exposure (R _air/R _gas), was measured for TSN 11 at 11 for 1,000 ppm CO exposure. All types of sensors showed good thermal stability. The electrochemical impedance spectroscopy (EIS) measurements were performed in different gas atmospheres (air, O₂, CO and NO₂) to better understand the electrical properties of the nanostructured mixed metal oxides.     

Design of an enhanced access point to optimize TCP performance in Wi-Fi hotspot networks

In the last years, the number of Wi-Fi hotspots at public venues has undergone a substantial growth, promoting the WLAN technologies as the ubiquitous solution to provide high-speed wireless connectivity in public areas. However, the adoption of a random access CSMA-based paradigm for the 802.11 MAC protocol makes difficult to ensure high throughput and a fair allocation of radio resources in 802.11-based WLANs. In this paper we evaluate extensively via simulations the interaction between the flow control mechanisms implemented at the TCP layer and the contention avoidance techniques used at the 802.11 MAC layer. We conducted our study considering initially M wireless stations performing downloads from the Internet. From our results, we observed that the TCP downlink throughput is not limited by the collision events, but by the inability of the MAC protocol to assign a higher chance of accessing the channel to the base station. We propose a simple and easy to implement modification of the base station’s behavior with the purpose of increasing the TCP throughput reducing useless MAC protocol overheads. With our scheme, the base station is allowed to transmit periodically bursts of data frames towards the mobile hosts. We design a resource allocation protocol aimed at maximizing the success probability of the uplink transmissions by dynamically adapting the burst length to the collision probability estimated by the base station. By its design, our scheme is also beneficial to achieve a fairer allocation of the channel bandwidth among the downlink and uplink flows, and among TCP and UDP flows. Simulation results confirm both the improvement in the TCP downlink throughput and the reduction of system unfairness.     

A support vector machine integrated system for the classification of operation anomalies in nuclear components and systems

A support vector machine (SVM) approach to the classification of transients in nuclear power plants is presented. SVM is a machine-learning algorithm that has been successfully used in pattern recognition for cluster analysis. In the present work, single- and multiclass SVM are combined into a hierarchical structure for distinguishing among transients in nuclear systems on the basis of measured data. An example of application of the approach is presented with respect to the classification of anomalies and malfunctions occurring in the feedwater system of a boiling water reactor. The data used in the example are provided by the HAMBO simulator of the Halden Reactor Project.     

Optimizing a remotely sensed proxy for plankton biomass in Lake Kivu

Many regions of the world, including inland lakes, present with suboptimal conditions for the remotely sensed retrieval of optical signals, thus challenging the limits of available satellite data-processing tools, such as atmospheric correction models (ACM) and water constituent-retrieval (WCR) algorithms. Working in such regions, however, can improve our understanding of remote-sensing tools and their applicability in new contexts, in addition to potentially offering useful information about aquatic ecology. Here, we assess and compare 32 combinations of two ACMs, two WCRs, and three binary categories of data quality standards to optimize a remotely sensed proxy of plankton biomass in Lake Kivu. Each parameter set is compared against the available ground-truth match-ups using Spearman’s right-tailed ρ. Focusing on the best sets from each ACM–WCR combination, their performances are discussed with regard to data distribution, sample size, spatial completeness, and seasonality. The results of this study may be of interest both for ecological studies on Lake Kivu and for epidemiological studies of disease, such as cholera, the dynamics of which has been associated with plankton biomass in other regions of the world.     

Quad‐Mesh Generation and Processing: A Survey

Triangle meshes have been nearly ubiquitous in computer graphics, and a large body of data structures and geometry processing algorithms based on them has been developed in the literature. At the same time, quadrilateral meshes, especially semi‐regular ones, have advantages for many applications, and significant progress was made in quadrilateral mesh generation and processing during the last several years. In this survey we discuss the advantages and problems of techniques operating on quadrilateral meshes, including surface analysis and mesh quality, simplification, adaptive refinement, alignment with features, parametrisation and remeshing.     

Local SVM approaches for fast and accurate classification of remote-sensing images

In this article, the task of remote-sensing image classification is tackled with local maximal margin approaches. First, we introduce a set of local kernel-based classifiers that alleviate the computational limitations of local support vector machines (SVMs), maintaining at the same time high classification accuracies. Such methods rely on the following idea: (a) during training, build a set of local models covering the considered data and (b) during prediction, choose the most appropriate local model for each sample to evaluate. Additionally, we present a family of operators on kernels aiming to integrate the local information into existing (input) kernels in order to obtain a quasi-local (QL) kernel. To compare the performances achieved by the different local approaches, an experimental analysis was conducted on three distinct remote-sensing data sets. The obtained results show that interesting performances can be achieved in terms of both classification accuracy and computational cost.     

Multirobot motion coordination in space and time

This paper describes a solution to the multirobot motion planning problem based on a decoupled analysis in the space domain and in the time domain. It investigates the practical use of the notion of motion plan quality and of the motion plan robustness measures for computing safe motions. The use of anytime algorithms allows one to evaluate the opportunity of looking for alternative solution paths by generating small variations of robot motions affecting both its geometrical path and its scheduled velocity. By using the concept of plan robustness, several alternative paths are generated and evaluated through various performance indices and impact factors, using heuristic rules. These indices allow one to know how much a variation affects a given plan. Finally, some recent experiments are outlined.     

User modeling in an OAM support system

This paper outlines studies relative to a research project on the development of a system that permits different people of an enterprise, with different roles and experience, to gain an adequate and personalized vision of the real status of the network and managed systems. What has been achieved is an easy to handle system, which permits quick and simple queries without utilizing complex man machine languages, and tailors personalized answers on the basis of the characteristics of the user. To achieve these results, User Modeling aspects have been considered and pursued.     

Synthetic Peroxides Promote Apoptosis of Cancer Cells by Inhibiting P-Glycoprotein ABCB5

This article discloses a new horizon for the application of peroxides in medical chemistry. Stable cyclic peroxides are demonstrated to have cytotoxic activity against cancer cells; in addition a mechanism of cytotoxic action is proposed. Synthetic bridged 1,2,4,5-tetraoxanes and ozonides were effective against HepG2 cancer cells and some ozonides selectively targeted liver cancer cells (the selectivity indexes for compounds 11 b and 12 a are 8 and 5, respectively). In some cases, tetraoxanes and ozonides were more selective than paclitaxel, artemisinin, and artesunic acid. Annexin V flow-cytometry analysis revealed that the active ozonides 22 a and 23 a induced cell death of HepG2 by apoptosis. Further study showed that compounds 22 a and 23 a exhibited a strong inhibitory effect on P-glycoprotein (P-gp/ABCB5)-overexpressing HepG2 cancer cells. ABCB5 is a key player in the multidrug-resistant phenotype of liver cancer. Peroxides failed to demonstrate a direct correlation between oxidative potential and their biological activity. To our knowledge this is the first time that peroxide diastereoisomers have been found to show stereospecific antimalarial action against the chloroquine-sensitive 3D7 strain of Plasmodium falciparum. Stereoisomeric ozonide 12 b is 11 times more active than stereoisomeric ozonide 12 a (IC₅₀=5.81 vs 65.18 μm ). Current findings mean that ozonides merit further investigation as potential therapeutic agents for drug-resistant hepatocellular carcinoma.     

Tree-Based Credal Networks for Classification

Bayesian networks are models for uncertain reasoning which are achieving a growing importance also for the data mining task of classification. Credal networks extend Bayesian nets to sets of distributions, or credal sets. This paper extends a state-of-the-art Bayesian net for classification, called tree-augmented naive Bayes classifier, to credal sets originated from probability intervals. This extension is a basis to address the fundamental problem of prior ignorance about the distribution that generates the data, which is a commonplace in data mining applications. This issue is often neglected, but addressing it properly is a key to ultimately draw reliable conclusions from the inferred models. In this paper we formalize the new model, develop an exact linear-time classification algorithm, and evaluate the credal net-based classifier on a number of real data sets. The empirical analysis shows that the new classifier is good and reliable, and raises a problem of excessive caution that is discussed in the paper. Overall, given the favorable trade-off between expressiveness and efficient computation, the newly proposed classifier appears to be a good candidate for the wide-scale application of reliable classifiers based on credal networks, to real and complex tasks.