An Odor Interaction Model of Binary Odorant Mixtures by a Partial Differential Equation Method

A novel odor interaction model was proposed for binary mixtures of benzene and substituted benzenes by a partial differential equation (PDE) method. Based on the measurement method (tangent-intercept method) of partial molar volume, original parameters of corresponding formulas were reasonably displaced by perceptual measures. By these substitutions, it was possible to relate a mixture’s odor intensity to the individual odorant’s relative odor activity value (OAV). Several binary mixtures of benzene and substituted benzenes were respectively tested to establish the PDE models. The obtained results showed that the PDE model provided an easily interpretable method relating individual components to their joint odor intensity. Besides, both predictive performance and feasibility of the PDE model were proved well through a series of odor intensity matching tests. If combining the PDE model with portable gas detectors or on-line monitoring systems, olfactory evaluation of odor intensity will be achieved by instruments instead of odor assessors. Many disadvantages (e.g., expense on a fixed number of odor assessors) also will be successfully avoided. Thus, the PDE model is predicted to be helpful to the monitoring and management of odor pollutions

Environmental Odour

Environmental odour is perceived as a major nuisance by the rural and urban population [...

The Regular Interaction Pattern among Odorants of the Same Type and Its Application in Odor Intensity Assessment

The olfactory evaluation function (e.g., odor intensity rating) of e-nose is always one of the most challenging issues in researches about odor pollution monitoring. But odor is normally produced by a set of stimuli, and odor interactions among constituents significantly influenced their mixture’s odor intensity. This study investigated the odor interaction principle in odor mixtures of aldehydes and esters, respectively. Then, a modified vector model (MVM) was proposed and it successfully demonstrated the similarity of the odor interaction pattern among odorants of the same type. Based on the regular interaction pattern, unlike a determined empirical model only fit for a specific odor mixture in conventional approaches, the MVM distinctly simplified the odor intensity prediction of odor mixtures. Furthermore, the MVM also provided a way of directly converting constituents’ chemical concentrations to their mixture’s odor intensity. By combining the MVM with usual data-processing algorithm of e-nose, a new e-nose system was established for an odor intensity rating. Compared with instrumental analysis and human assessor, it exhibited accuracy well in both quantitative analysis (Pearson correlation coefficient was 0.999 for individual aldehydes (n = 12), 0.996 for their binary mixtures (n = 36) and 0.990 for their ternary mixtures (n = 60)) and odor intensity assessment (Pearson correlation coefficient was 0.980 for individual aldehydes (n = 15), 0.973 for their binary mixtures (n = 24), and 0.888 for their ternary mixtures (n = 25)). Thus, the observed regular interaction pattern is considered an important foundation for accelerating extensive application of olfactory evaluation in odor pollution monitoring

Influence of micro-rolling on the strength and ductility of plasma-arc additively manufactured Ti–6Al–4V alloys

The aim of this study was to reduce the number of defects, refine the grain size, and improve the mechanical properties of Ti–6Al–4V. We investigated the effect of interpass micro-rolling on the microstructures and mechanical properties of Ti–6Al–4V alloys produced via plasma arc additive manufacturing (PAAM). The non-rolled plasma-arc additively manufactured (PAAMed) sample exhibited coarse columnar crystals, basket-weave microstructures, and Widmanstätten structures, while the interpass-micro-rolled PAAMed sample exhibited fine equiaxed crystals and basket-weave microstructures. Interpass micro-rolling could significantly reduce the number density and size of defects during PAAM, and the rolled samples exhibited lower mechanical property anisotropy than the non-rolled samples. Moreover, the rolled sample exhibited a higher yield strength (∼797.66 and 793.45 MPa), tensile strength (∼939.73 and 935.71 MPa), and elongation (∼12.83% and 14.73%) in the X- and Z-directions than the unrolled sample. These enhanced mechanical properties can be attributed to αGB fragmentation, grain boundary strengthening, dislocation strengthening, and a low crack density. The micro-cracks preferentially nucleated around defects in the non-rolled sample, resulting in high crack and defect densities

Image Segmentation from Sparse Decomposition with a Pretrained Object-Detection Network

Annotations for image segmentation are expensive and time-consuming. In contrast to image segmentation, the task of object detection is in general easier in terms of the acquisition of labeled training data and the design of training models. In this paper, we combine the idea of unsupervised learning and a pretrained object-detection network to perform image segmentation, without using expensive segmentation labels. Specially, we designed a pretext task based on the sparse decomposition of object instances in videos to obtain the segmentation mask of the objects, which benefits from the sparsity of image instances and the inter-frame structure of videos. To improve the accuracy of identifying the ’right’ object, we used a pretrained object-detection network to provide the location information of the object instances, and propose an Object Location Segmentation (OLSeg) model of three branches with bounding box prior. The model is trained from videos and is able to capture the foreground, background and segmentation mask in a single image. The performance gain benefits from the sparsity of object instances (the foreground and background in our experiments) and the provided location information (bounding box prior), which work together to produce a comprehensive and robust visual representation for the input. The experimental results demonstrate that the proposed model boosts the performance effectively on various image segmentation benchmarks

Image Segmentation from Sparse Decomposition with a Pretrained Object-Detection Network

Annotations for image segmentation are expensive and time-consuming. In contrast to image segmentation, the task of object detection is in general easier in terms of the acquisition of labeled training data and the design of training models. In this paper, we combine the idea of unsupervised learning and a pretrained object-detection network to perform image segmentation, without using expensive segmentation labels. Specially, we designed a pretext task based on the sparse decomposition of object instances in videos to obtain the segmentation mask of the objects, which benefits from the sparsity of image instances and the inter-frame structure of videos. To improve the accuracy of identifying the ’right’ object, we used a pretrained object-detection network to provide the location information of the object instances, and propose an Object Location Segmentation (OLSeg) model of three branches with bounding box prior. The model is trained from videos and is able to capture the foreground, background and segmentation mask in a single image. The performance gain benefits from the sparsity of object instances (the foreground and background in our experiments) and the provided location information (bounding box prior), which work together to produce a comprehensive and robust visual representation for the input. The experimental results demonstrate that the proposed model boosts the performance effectively on various image segmentation benchmarks