Using fuzzy coding with qualitative data: example with subjective data in human-computer interaction

This article shows the role that fuzzy sets may play in the prospect of analysing qualitative data. To underline this role, a human-computer interaction (HCI) study is presented. The data coming from 20 experts concerns their judgment regarding 33 questions related to the use of HCI approaches in order to support interactive system development phases. Each response scale features three main modalities, that is Agree, Partially agree and Disagree. The dataset example is analysed using multiple correspondence analysis (MCA) with both crisp and fuzzy coding models where the intermediate modality, Partially agree, is removed and considered with ½ membership values to the two extreme modalities. A comparative analysis is performed and the discussion states the interest of fuzzy coding with several kinds of qualitative factors or measurement variables. With qualitative measurement variables (our example), the main drawback of fuzzy coding could be the information loss, which is counterbalanced by the possibility of having fewer modalities and therefore of simplifying the multivariate analysis.     

COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

The purpose of this research is the segmentation of lungs computed tomography (CT) scan for the diagnosis of COVID-19 by using machine learning methods. Our dataset contains data from patients who are prone to the epidemic. It contains three types of lungs CT images (Normal, Pneumonia, and COVID-19) collected from two different sources; the first one is the Radiology Department of Nishtar Hospital Multan and Civil Hospital Bahawalpur, Pakistan, and the second one is a publicly free available medical imaging database known as Radiopaedia. For the preprocessing, a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an automated region of interest (ROIs) and acquire 52 hybrid statistical features for each ROIs. Also, 12 optimized statistical features are selected via the chi-square feature reduction technique. For the classification, five machine learning classifiers named as deep learning J4, multilayer perceptron, support vector machine, random forest, and naive Bayes are deployed to optimize the hybrid statistical features dataset. It is observed that the deep learning J4 has promising results (sensitivity and specificity: 0.987; accuracy: 98.67%) among all the deployed classifiers. As a complementary study, a statistical work is devoted to the use of a new statistical model to fit the main datasets of COVID-19 collected in Pakistan.     

Gaussian Product Sampling for Rendering Layered Materials

To increase diversity and realism, surface bidirectional scattering distribution functions (BSDFs) are often modelled as consisting of multiple layers, but accurately evaluating layered BSDFs while accounting for all light transport paths is a challenging problem. Recently, Guo et al. [GHZ18] proposed an accurate and general position-free Monte Carlo method, but this method introduces variance that leads to longer render time compared to non-stochastic layered models. We improve the previous work by presenting two new sampling strategies, pair-product sampling and multiple-product sampling. Our new methods better take advantage of the layered structure and reduce variance compared to the conventional approach of sequentially sampling one BSDF at a time. Our pair-product sampling strategy importance samples the product of two BSDFs from a pair of adjacent layers. We further generalize this to multiple-product sampling, which importance samples the product of a chain of three or more BSDFs. In order to compute these products, we developed a new approximate Gaussian representation of individual layer BSDFs. This representation incorporates spatially varying material properties as parameters so that our techniques can support an arbitrary number of textured layers. Compared to previous Monte Carlo layering approaches, our results demonstrate substantial variance reduction in rendering isotropic layered surfaces.     

Binary Ostensibly-Implicit Trees for Fast Collision Detection

We present a simple, efficient and low-memory technique, targeting fast construction of bounding volume hierarchies (BVH) for broad-phase collision detection. To achieve this, we devise a novel representation of BVH trees in memory. We develop a mapping of the implicit index representation to compact memory locations, based on simple bit-shifts, to then construct and evaluate bounding volume test trees (BVTT) during collision detection with real-time performance. We model the topology of the BVH tree implicitly as binary encodings which allows us to determine the nodes missing from a complete binary tree using the binary representation of the number of missing nodes. The simplicity of our technique allows for fast hierarchy construction achieving over 6× speedup over the state-of-the-art. Making use of these characteristics, we show that not only it is feasible to rebuild the BVH at every frame, but that using our technique, it is actually faster than refitting and more memory efficient.     

Model and methods to address urban road network problems with disruptions

Disruptions in urban road networks can quickly and significantly reduce the quality of the whole transportation network, and impact urban mobility for light vehicles, public transportation, etc. In this study, we consider both unidirectional and multidirectional road network problems with disruptions and connecting requirements. These problems aim at reconfiguring the urban network in terms of road direction in order to maintain a path among all points of the network (strong connectivity). The former is defined on simple graphs, mainly modeling part of a city such as historical centers, while the latter relies on multigraphs, modeling more general networks. Restoring the network (strong connectivity) after some disruptions may require the modification of the orientation of some streets, that is, arc reversals. Such actions can disturb users' driving habits. Thus, two objectives are considered separately: minimizing the total travel distance and minimizing the number of arc reversals. We define formally both problems and propose two metaheuristics, a biased random key genetic algorithm and an iterated local search. Numerical experiments have been performed on a set of generated instances and on the urban network of Troyes (France).     

Room temperature molten salts as lithium battery electrolyte

In the present study, are reported investigations obtained with the room temperature molten salt (RTMS) ethyl-methyl-imidazolium bis-(trifluoromethanesulfonyl)-imide (EMI-TFSI) in order to use it as solvent in lithium battery. The thermal stability, viscosity, conductivity and electrochemical properties are presented. A solution of 1m lithium bis-(trifluoromethanesulfonyl)-imide (LiTFSI) in EMI-TFSI has been used to test the electrolyte in a battery with LiCoO₂ and Li₄Ti₅O₁₂ as respectively cathode and anode materials. Cycling and power measurements have been obtained. The results have been compared with those obtained with a molten salt formulated with a different anion, BF₄⁻ and with a conventional liquid organic solvent EC/DMC containing LiTFSI. The 1m LiTFSI/EMI-TFSI electrolyte provides the best cycling performance: a capacity up to 106 mAh g⁻¹ is still delivered after 200 cycles, with 1C rate at 25 °C.     

Development and Pheromone Communication Systems in Hybrids of Agrotis ipsilon and Agrotis segetum (Lepidoptera: Noctuidae)

Hybrids were obtained by crossing males of the turnip moth Agrotis segetum, which has a period of arrested development in the larval stage, with females of the migrant and nondiapausing black cutworm, Agrotis ipsilon. No progeny were obtained by crossing females of A. segetum with males of A. ipsilon. Backcrosses were successful only by crossing hybrids with both sexes of A. ipsilon. No larval oligopause occurred when hybrid larvae were reared under conditions inducing arrest in A. segetum (12L:12D, 21°C). Wind tunnel tests showed interattractiveness of F₁ hybrids with A. ipsilon but not with A. segetum. Single sensillum recordings of pheromone-sensitive hairs on the antennae of males of the parent species revealed differences in the distribution of sensilla. F₁ and F₂ hybrids exhibited the same receptor cell types and distribution as A. ipsilon. Gas chromatographic analysis of female pheromone gland extracts revealed that F₁ hybrids were also very close to A. ipsilon.     

Species-specific stable isotope analysis by the hyphenation of chromatographic techniques with MC-ICPMS

This work reviews the basis and all the existing publications on the hyphenation of chromatography-based techniques to MC-ICPMS for isotopic studies that were published until the end of 2010. A brief historical retrospective of the measurement of isotope ratios from transient signals by ICPMS with different sample introduction techniques is also included. The most important experimental parameters and data reduction strategies affecting the accurate and precise measurement of compound-specific isotope ratios by either HPLC or GC coupled to MC-ICPMS are discussed. All the applications are reported and critically reviewed in terms of analytical characteristics, performances, optimization, advantages and disadvantages and future applicability to the environmental, geochemical, or bioinorganic studies.