A Feature Selection Strategy to Optimize Retinal Vasculature Segmentation

Diabetic retinopathy (DR) is a complication of diabetes mellitus that appears in the retina. Clinitians use retina images to detect DR pathological signs related to the occlusion of tiny blood vessels. Such occlusion brings a degenerative cycle between the breaking off and the new generation of thinner and weaker blood vessels. This research aims to develop a suitable retinal vasculature segmentation method for improving retinal screening procedures by means of computer-aided diagnosis systems. The blood vessel segmentation methodology relies on an effective feature selection based on Sequential Forward Selection, using the error rate of a decision tree classifier in the evaluation function. Subsequently, the classification process is performed by three alternative approaches: artificial neural networks, decision trees and support vector machines. The proposed methodology is validated on three publicly accessible datasets and a private one provided by Hospital Sant Joan of Reus. In all cases we obtain an average accuracy above 96% with a sensitivity of 72% in the blood vessel segmentation process. Compared with the state-of-the-art, our approach achieves the same performance as other methods that need more computational power. Our method significantly reduces the number of features used in the segmentation process from 20 to 5 dimensions. The implementation of the three classifiers confirmed that the five selected features have a good effectiveness, independently of the classification algorithm.     

A reference isotope dilution headspace GC/MS method for the determination of nitrite and nitrate in meat samples

A novel method for the determination of nitrite and nitrate in meat products is presented. The samples were ground and extracted in hot water with the presence of and internal standards. The solution was buffered with sodium bicarbonate and reacted with triethyloxonium tetrafluoroborate to convert nitrite and nitrate into EtNO₂ and EtONO₂. Such derivatives could be detected by headspace GC/MS in positive chemical ionisation mode with 0.05 µg g⁻¹ and 1.0 µg g⁻¹ LOD. The method was used for and quantitation in the 0.5-300 and 2.5-300 µg g⁻¹ ranges. The method was applied for the analysis of fifteen meat products. Despite minimal sample preparation, the headspace sampling ensured a clean chromatography for over 135 analyses (throughput ten samples per hour). The proposed method offers selective GC/MS detection combined with high-precision isotope dilution calibration, it is suitable for metrological applications and can support regulations on meat safety (European Commission, 2011).     

Modelling the propagation of social response during a disease outbreak

Epidemic trajectories and associated social responses vary widely between populations, with severe reactions sometimes observed. When confronted with fatal or novel pathogens, people exhibit a variety of behaviours from anxiety to hoarding of medical supplies, overwhelming medical infrastructure and rioting. We developed a coupled network approach to understanding and predicting social response. We couple the disease spread and panic spread processes and model them through local interactions between agents. The social contagion process depends on the prevalence of the disease, its perceived risk and a global media signal. We verify the model by analysing the spread of disease and social response during the 2009 H1N1 outbreak in Mexico City and 2003 severe acute respiratory syndrome and 2009 H1N1 outbreaks in Hong Kong, accurately predicting population-level behaviour. This kind of empirically validated model is critical to exploring strategies for public health intervention, increasing our ability to anticipate the response to infectious disease outbreaks.     

Sensitivity-based conceptual design and tolerance allocation using the continuous ants colony algorithm (CACO)

In an assembly, there are two ways to control the deviation of critical dimensions. One is by keeping the deviation of the critical dimension small by tightening manufacturing tolerances and controlling aging and environmental effects. This approach is traditional and expensive, as it requires tighter manufacturing tolerances and protection from aging and the environment. The second is by moving the nominal values of the non-critical dimensions to a less sensitive portion. This approach is very helpful in improving the quality with no additional cost. One can analyze any number of designs very early in the concept development stage of a project. After the concept design the cost-based optimal tolerances for the corresponding dimensions are allocated. The continuous ants colony algorithm, a kind of meta-heuristic approach, is used as an optimization tool for minimizing the critical dimension deviation and allocating the cost- based optimal tolerances.     

Thermal transport properties of carbon-assisted phase change nanocomposite

Abstract

In this study, stability, density, rheology, and thermal conductivity of the Multi-wall carbon nanotubes (MWCNTs) based PCM nanocomposites were experimentally investigated. The PCM OM08 has been selected for the air-conditioning application with a phase change temperature of 8–10?°C. PCM nanocomposites (without surfactant) in various concentrations namely 0.05, 0.1, 0.3 and 0.5?vol. % were prepared using a typical two-step method and high homogenous stability is achieved using a mechanical mixing technique (ultra-sonication). The density of PCM nanocomposites was measured using two different types of standard volumetric flask approach and the experimental values confirmed good conformity with the Pak and Cho mixing theory. The highest thermal conductivity augmentation of 24.03%and 64.04% were achieved in liquid and solid states, respectively, with 0.5?vol. % of MWCNT. While, the addition of MWCNT resulted in a changeover of Newtonian to Non-Newtonian behaviors at a low shear rate, and the dynamic viscosity enhancement was increased by 130% with 0.5?vol. % of MWCNT. Further, the results were compared with existing correlations and it was found that the experimental values were in line with existing correlations.     

Region based coronary artery segmentation using modified Frangi's vesselness measure

Recent research suggests that the cardiovascular diseases (CVDs), seem to be the foremost cause of mortality among the world populace. Three dimensional (3D) imaging modality such as computed tomography angiography(CTA) is a standard noninvasive imaging modality which has great potentials for the visualization of heart and coronary arteries. This article presents a fully automated method for coronary artery extraction using modified Frangi's vesselness measure and region based segmentation. In this article, grayness and gradient based measures are used while computing Frangi's vesselness measure to improve the extraction of coronary arteries. The obtained vesselness measures are utilized for automatically computing the location of ostia. The locations of ostia are then used as starting seed points in region growing segmentation to extract coronary arteries. Three major coronary arteries, namely the left anterior descending artery (LAD), left circumflex artery (LCX) and right coronary artery (RCA) are segmented using the proposed method and the centerlines are extracted for the main coronary branches. The performance of the proposed method is evaluated using 12 3D CCTA data set. The experimental results reveal that during the calculation of modified Frangi's vesselness measure the proposed method gives improved results. The qualitative results obtained during the segmentation stage are also convincing. The average segmentation accuracy and overlap measure of the proposed method are 97.4% and 77.86%, respectively. Hence, the proposed automated approach can detect and extract coronary arteries in CCTA images with high performance.     

Hydrothermally synthesized ZnO and Z-rGO nanorods: Effect of post-annealing temperature and rGO incorporation on hydrogen sensing

Journal of Materials Science: Materials in Electronics - In this work, hydrogen-sensing characteristics of zinc oxide nanorods and reduced graphene oxide-incorporated zinc oxide nanorods are...     

Scalable Lanthanum Titanate (La2Ti2O7) nanostructures as UV photodetectors

Thermally and chemically stable perovskite-like layer structures have attracted extensively in the field of energy and environmental applications. In this study, La₂Ti₂O₇ was synthesized by the solvothermal method at 180 °C. This method provides high pure and homogeneously dispersed nanorods of orthorhombic phase having length of 250 nm and width of 70 nm. Even though this is a low-temperature synthesis method, it yields high crystalline nature after calcination. The novelty of this work is its synthesis methodology by the solvothermal route to achieve lower weight loss of La₂Ti₂O₇. Furthermore, they exhibit narrow absorption in the UV-region from 200 to 350 nm, makes it possible to fabricate it as UV photodetector at ambient condition. In presence of UV illumination at 390 nm, it shows sharp photocurrent response with the decay time of 1.7 s.

     

Improvement of battery lifetime in software-defined network using particle swarm optimization based cluster-head gateway switch routing protocol with fuzzy rules

The software-defined network (SDN) is one of the network architectures, in which the data plane and control plane is divided from each other, and the network can be handled using a sensibly centralized controller and this method is adopted to reconfigure the wireless sensor network automatically. In this article, to implement the SDN in MANET, in which control nodes can be chosen in SDN dynamically for the activation of MANET function to allocate the works to other mobile nodes to the base station. However, in the field of mobile ad hoc networks, the network lifetime, and battery lifetime is one of the major problems and the energy consumption can play a significant rule for the transmission of data in the SDN. Therefore, in this article, particle swarm optimization (PSO) based CGSR (cluster-head gateway switch routing protocol) algorithm with fuzzy rules is proposed to increase the network lifetime of battery powered mobile nodes by reducing the energy consumptions of each node in software-defined MANET. In this proposed method, a routing method that can permit various mobile nodes with low battery power to transmits the data from source node to base station. We design a PSO based CGSR routing protocol by selecting the routing mobile nodes using fuzzy rules for packet transmission. In CGSR process, the formation of cluster and selection of cluster head is executed depending on the particle swarm optimization method. This proposed routing protocol can be used to enhance the battery lifetime by extension of the network lifetime with numerical analysis for efficient route node selection.