Rotating tongues (Protrudences) of ozone- poor air in the Antarctic ozone hole, sweeping over lower latitudes : signatures in ground-based Dobson data

Using data from ground-based Dobson spectrophotometers, the evolution of Antarctic ozone holes during the southern springs of 1992, 1993, 1994 and 1995 was studied, At the South Pole, the evolution was mostly smooth, steady decrease up to about September end and a steady recovery up to about December end, At latitudes near 65°5, the ozone levels at different latitudes and longitudes showed fluctuations compatible with passing of a noncircular (oval) vortex boundary, (edge, rotating tongue), with a rotation period of 15-20 days, However, often there were depletions in-between, extending to lower latitudes up to ~30°S, indicating corrugations in the oval boundary with effects equivalent to those of more than one rotating tongue, There were other short- spaced (5-8 days) depletions, not necessarily simultaneous at different latitudes in the same longitude, and more copious at lower latitudes, probably indicating the effects of synoptic disturbances on total ozone through tropopause pressure changes and/or ozone mini-holes caused by anticyclonic tropospheric forcing under the southern polar vortex.

Triangulability of convex graphs and convex skewness

Suppose [Formula: see text] is a subgraph of a convex complete graph [Formula: see text] and [Formula: see text] contains no boundary edge of [Formula: see text] and [Formula: see text]. We determine necessary and sufficient conditions on [Formula: see text] such that [Formula: see text] admits a triangulation. For [Formula: see text], we investigate the possibility of placing [Formula: see text] in [Formula: see text] such that [Formula: see text] admits a triangulation for certain families of graphs [Formula: see text]. These results are then applied to determine the convex skewness of the convex graphs of the form [Formula: see text].

Phase-field gradient theory

We propose a phase-field theory for enriched continua. To generalize classical phase-field models, we derive the phase-field gradient theory based on balances of microforces, microtorques, and mass. We focus on materials where second gradients of the phase field describe long-range interactions. By considering a nontrivial interaction inside the body, described by a boundary-edge microtraction, we characterize the existence of a hypermicrotraction field, a central aspect of this theory. On surfaces, we define the surface microtraction and the surface-couple microtraction emerging from internal surface interactions. We explicitly account for the lack of smoothness along a curve on surfaces enclosing arbitrary parts of the domain. In these rough areas, internal-edge microtractions appear. We begin our theory by characterizing these tractions. Next, in balancing microforces and microtorques, we arrive at the field equations. Subject to thermodynamic constraints, we develop a general set of constitutive relations for a phase-field model where its free-energy density depends on second gradients of the phase field. A priori, the balance equations are general and independent of constitutive equations, where the thermodynamics constrain the constitutive relations through the free-energy imbalance. To exemplify the usefulness of our theory, we generalize two commonly used phase-field equations. We propose a ‘generalized Swift–Hohenberg equation’—a second-grade phase-field equation—and its conserved version, the ‘generalized phase-field crystal equation’—a conserved second-grade phase-field equation. Furthermore, we derive the configurational fields arising in this theory. We conclude with the presentation of a comprehensive, thermodynamically consistent set of boundary conditions.

Performance Analysis of Glioma Brain Tumor Segmentation Using Ridgelet Transform and Co-Active Adaptive Neuro Fuzzy Expert System Methodology

Objective: The Glioma brain tumor detection and segmentation methods are proposed in this paper using machine learning approaches. The primary objective of this paper is to provide high level of tumor region segmentation using optimization and machine learning techniques. Methods: The boundary edge pixels are detected using Kirsch's edge detectors and then contrast adaptive histogram equalization method is applied on the edge detected pixels. Then, Ridgelet transform is applied on this enhanced brain image in order to obtain the Ridgelet multi resolution coefficients. Further, features are derived from the Ridgelet transformed coefficients and the features are optimized using Principal Component Analysis (PCA) method and these optimized features are classified into Glioma or non-Glioma brain images using Co-Active Adaptive Neuro Fuzzy Expert System (CANFES) classifier. Results: The proposed method with PCA and CANFES classification approach obtains 97.6% of sensitivity (Se), 98.56% of Specificity (sp), 98.73% of Accuracy (Acc), 98.85% of Precision (Pr), 98.11% of False Positive Rate (FPR) and 98.185 of False Negative Rate (FNR), then the proposed Glioma brain tumor detection method using CANFES classification approach only.

A partial inverse problem for quantum graphs with a loop

We consider the Sturm–Liouville operator on quantum graphs with a loop with the standard matching conditions in the internal vertex and the jump conditions at the boundary vertex. Given the potential on the loop, we try to recover the potential on the boundary edge from the subspectrum. The uniqueness theorem and a constructive algorithm for the solution of this partial inverse problem are provided.

Partial inverse problems for quadratic differential pencils on a graph with a loop

In this paper, partial inverse problems for the quadratic pencil of Sturm–Liouville operators on a graph with a loop are studied. These problems consist in recovering the pencil coefficients on one edge of the graph (a boundary edge or the loop) from spectral characteristics, while the coefficients on the other edges are known a priori. We obtain uniqueness theorems and constructive solutions for partial inverse problems.

Performance Analysis of Glioma Brain Tumor Segmentation using Ridgelet Transform and CANFES Methodology

Objective:The Glioma brain tumor detection and segmentation methods are proposed in this paper using machine learning approaches. Methods:The boundary edge pixels are detected using Kirsch’s edge detectors and then contrast adaptive histogram equalization method is applied on the edge detected pixels. Then, Ridgelet transform is applied on this enhanced brain image in order to obtain the Ridgelet multi resolution coefficients. Further, features are derived from the Ridgelet transformed coefficients and the features are optimized using Principal Component Analysis (PCA) method and these optimized features are classified into Glioma or non-Glioma brain images using Co-Active Adaptive Neuro Fuzzy Expert System (CANFES) classifier.Results:The proposed method with PCA and CANFES classification approach obtains 97.6% of se, 98.56% of sp, 98.73% of Acc, 98.85% of Pr, 98.11% of FPR and 98.185 of FNR, then the proposed Glioma brain tumor detection method using CANFES classification approach only.

Wrinkle and boundary detection of fiber products in robotic composites manufacturing

Purpose The paper aims to focus on a vision-based approach to advance the automated process of the manufacturing of an Airbus A350’s pressure bulkhead. The setup enables automated deformation and draping of a fiber textile on a form-variable end-effector. Design/methodology/approach The proposed method uses the information of infrared (IR) and color-based images in Red, Green and Blue (RGB) representative format, as well as depth measurements to identify the wrinkles and boundary edge of semi-finished dry fiber products on the double-curved surface of a flexible modular gripper used for laying the fabric. The technique implements a simple and practical image processing solution using a sequence of pixel-wise binary masks on an industrial scale setup; it bridges the gap between laboratory experiments and real-world execution, thereby demonstrating practical and applied research. Findings The efficacy of the technique is demonstrated via experiments in the presented work. The two objectives as follows boundary edge detection and wrinkle detection are accomplished in real time in an industrial setup. Originality/value During the draping process, tensions developed in the fibers of the textile cause wrinkles on the surface, which are highly detrimental to the production process, material quality and strength. The proposed method automates the identification and detection of the wrinkles and the textile on the gripper surface. The proposed work aids in alleviating the problems caused by these wrinkles and helps in quality control in the production process.