SWGMM: a semi-wrapped Gaussian mixture model for clustering of circular–linear data

Finite mixture models are widely used to perform model-based clustering of multivariate data sets. Most of the existing mixture models work with linear data; whereas, real-life applications may involve multivariate data having both circular and linear characteristics. No existing mixture models can accommodate such correlated circular–linear data. In this paper, we consider designing a mixture model for multivariate data having one circular variable. In order to construct a circular–linear joint distribution with proper inclusion of correlation terms, we use the semi-wrapped Gaussian distribution. Further, we construct a mixture model (termed SWGMM) of such joint distributions. This mixture model is capable of approximating the distribution of multi-modal circular–linear data. An unsupervised learning of the mixture parameters is proposed based on expectation maximization method. Clustering is performed using maximum a posteriori criterion. To evaluate the performance of SWGMM, we choose the task of color image segmentation in LCH space. We present comprehensive results and compare SWGMM with existing methods. Our study reveals that the proposed mixture model outperforms the other methods in most cases.     

Design and Uncertainty Evaluation of a Strain Measurement System

Strain measurement is very important in various industrial applications as well as different disciplines of science and technology for direct and indirect observations of certain parameters. Designing signal conditioning circuit is always a challenging and important task for satisfactory and reliable performance of a sensor as well as the system. The design and implementation details of a signal conditioning circuit of resistive sensor (strain gauge) for strain measurement are presented in this paper. Also the important aspects in designing a signal conditioning circuit for resistive sensor are presented and a novel method for the measurement of strain is discussed. Quarter bridge configuration with AC voltage excitation is used for the measurement along with the necessary circuitry to get a suitable and measurable output DC voltage. The measurement system is calibrated using a cantilever of stainless steel and the details of calibration are presented in the paper. The uncertainty associated with the measurement system is evaluated.     

EMERS: a tree matching–based performance evaluation of mathematical expression recognition systems

Performance evaluation of mathematical expression recognition systems is attempted. The proposed method assumes expressions (input as well as recognition output) are coded following MathML or T_EX/LaT_EX (which also gets converted into MathML) format. Since any MathML representation follows a tree structure, evaluation of performance has been modeled as a tree-matching problem. The tree corresponding to the expression generated by the recognizer is compared with the groundtruthed one by comparing the corresponding Euler strings. The changes required to convert the tree corresponding to the expression generated by the recognizer into the groundtruthed one are noted. The number of changes required to make such a conversion is basically the distance between the trees. This distance gives the performance measure for the system under testing. The proposed algorithm also pinpoints the positions of the changes in the output MathML file. Testing of the proposed evaluation method considers a set of example groundtruthed expressions and their corresponding recognized results produced by an expression recognition system.     

COVID-19 Mimics Pulmonary Dysfunction in Muscular Dystrophy as a Post-Acute Syndrome in Patients

Although progressive wasting and weakness of respiratory muscles are the prominent hallmarks of Duchenne muscular dystrophy (DMD) and long-COVID (also referred as the post-acute sequelae of COVID-19 syndrome); however, the underlying mechanism(s) leading to respiratory failure in both conditions remain unclear. We put together the latest relevant literature to further understand the plausible mechanism(s) behind diaphragm malfunctioning in COVID-19 and DMD conditions. Previously, we have shown the role of matrix metalloproteinase-9 (MMP9) in skeletal muscle fibrosis via a substantial increase in the levels of tumor necrosis factor-α (TNF-α) employing a DMD mouse model that was crossed-bred with MMP9-knockout (MMP9-KO or MMP9^-/-) strain. Interestingly, recent observations from clinical studies show a robust increase in neopterin (NPT) levels during COVID-19 which is often observed in patients having DMD. What seems to be common in both (DMD and COVID-19) is the involvement of neopterin (NPT). We know that NPT is generated by activated white blood cells (WBCs) especially the M1 macrophages in response to inducible nitric oxide synthase (iNOS), tetrahydrobiopterin (BH4), and tetrahydrofolate (FH4) pathways, i.e., folate one-carbon metabolism (FOCM) in conjunction with epigenetics underpinning as an immune surveillance protection. Studies from our laboratory, and others researching DMD and the genetically engineered humanized (hACE2) mice that were administered with the spike protein (SP) of SARS-CoV-2 revealed an increase in the levels of NPT, TNF-α, HDAC, IL-1β, CD147, and MMP9 in the lung tissue of the animals that were subsequently accompanied by fibrosis of the diaphragm depicting a decreased oscillation phenotype. Therefore, it is of interest to understand how regulatory processes such as epigenetics involvement affect DNMT, HDAC, MTHFS, and iNOS that help generate NPT in the long-COVID patients.     

Validity of modified Gompertz and Logistic models in predicting cell growth of Pediococcus acidilactici H during the production of bacteriocin pediocin AcH

Data for the cell growth of Pediococcus acidilactici H during the fermentative production of bacteriocin, pediocin AcH (taken from a previous study) was modeled by two sigmoidal functions, modified Gompertz and Logistic. Results showed that the models could adequately account for the cell growth up to the growth phase, but failed to account for the stationary and death phase.     

Green synthesis of biocompatible gold nanoparticles using Fagopyrum esculentum leaf extract

This report describes the use of ethnolic extract of Fagopyrum esculentum leaves for the synthesis of gold nanoparticles. UV-visible spectroscopy analysis indicated the successful formation of gold nanoparticles. The synthesized nanoparticles were characterized by transmission electron microscopy (TEM), high resolution TEM (HRTEM) and were found to be spherical, hexagonal and triangular in shape with an average size of 8.3 nm. The crystalline nature of the gold nanoparticles was confirmed from X-ray diffraction (XRD) and selected-area electron diffraction (SAED) patterns. Fourier transform infrared (FT-IR) and energy-dispersive X-ray analysis (EDX) suggested the presence of organic biomolecules on the surface of the gold nanoparticles. Cytotoxicity tests against human HeLa, MCF-7 and IMR-32 cancer cell lines revealed that the gold nanoparticles were non-toxic and thus have potential for use in various biomedical applications.     

Simulation, modeling, and crystal growth of Cd0.9Zn0.1Te for nuclear spectrometers

High-quality, large (10 cm long and 2.5 cm diameter), nuclear spectrometer grade Cd_0.9Zn_0.1Te (CZT) single crystals have been grown by a controlled vertical Bridgman technique using in-house zone refined precursor materials (Cd, Zn, and Te). A state-of-the-art computer model, multizone adaptive scheme for transport and phase-change processes (MASTRAP), is used to model heat and mass transfer in the Bridgman growth system and to predict the stress distribution in the as-grown CZT crystal and optimize the thermal profile. The model accounts for heat transfer in the multiphase system, convection in the melt, and interface dynamics. The grown semi-insulating (SI) CZT crystals have demonstrated promising results for high-resolution room-temperature radiation detectors due to their high dark resistivity (ρ≈2.8 × 10¹¹ Θ cm), good charge-transport properties [electron and hole mobility-life-time product, μτ_e≈(2–5)×10⁻³ and μτ_h≈(3–5)×10⁻⁵ respectively, and low cost of production. Spectroscopic ellipsometry and optical transmission measurements were carried out on the grown CZT crystals using two-modulator generalized ellipsometry (2-MGE). The refractive index n and extinction coefficient k were determined by mathematically eliminating the ∼3-nm surface roughness layer. Nuclear detection measurements on the single-element CZT detectors with ²⁴¹Am and ¹³⁷Cs clearly detected 59.6 and 662 keV energies with energy resolution (FWHM) of 2.4 keV (4.0%) and 9.2 keV (1.4%), respectively.     

Analysis of Elevated Temperature Flow Behavior of 316LN Stainless Steel Under Compressive Loading

To understand the flow behavior of a newly developed austenitic stainless steel 316LN with 0.14 wt% nitrogen, isothermal compression tests have been carried out in the hot working domain. From the analysis of flow behavior, it is observed that the nitrogen enhanced steel, in its hot working domain exhibits strain and strain rate hardening with thermal softening. The flow behavior analysis also demonstrates the coupled effect of strain–temperature and strain rate temperature on the flow stress. To depict the flow behavior of the material, strain compensated Arrhenius (SCA) equation and Model D8A have been used. The SCA predicts the flow curves with an average absolute error of 9.27% and a correlation coefficient of 0.977, whereas the prediction by Model D8A gives the average absolute relative error as 10.86% with a correlation coefficient of 0.966. For high temperature and intermediate strain rate, both Model D8A and SCA equation depict the flow behavior of 316L (0.14)N SS with good correlation and generalization. However, at low temperature and high strain rate domain, both the models are unable to depict the behavior; this is attributed to the fact that the material shows two slope behaviors where the constants have been calculated assuming a linear relationship between stress and strain rate.     

A Wide Learning Approach for Interpretable Feature Recommendation for 1-D Sensor Data in IoT Analytics

Machine Intelligence Research - This paper presents a state of the art machine learning-based approach for automation of a varied class of Internet of things (IoT) analytics problems targeted on...