The Effect of Rider:Horse Bodyweight Ratio on the Superficial Body Temperature of Horse’s Thoracolumbar Region Evaluated by Advanced Thermal Image Processing

Appropriate matching of rider–horse sizes is becoming an increasingly important issue of riding horses’ care, as the human population becomes heavier. Recently, infrared thermography (IRT) was considered to be effective in differing the effect of 10.6% and 21.3% of the rider:horse bodyweight ratio, but not 10.1% and 15.3%. As IRT images contain many pixels reflecting the complexity of the body’s surface, the pixel relations were assessed by image texture analysis using histogram statistics (HS), gray-level run-length matrix (GLRLM), and gray level co-occurrence matrix (GLCM) approaches. The study aimed to determine differences in texture features of thermal images under the impact of 10–12%, >12 ≤15%, >15 <18% rider:horse bodyweight ratios, respectively. Twelve horses were ridden by each of six riders assigned to light (L), moderate (M), and heavy (H) groups. Thermal images were taken pre- and post-standard exercise and underwent conventional and texture analysis. Texture analysis required image decomposition into red, green, and blue components. Among 372 returned features, 95 HS features, 48 GLRLM features, and 96 GLCH features differed dependent on exercise; whereas 29 HS features, 16 GLRLM features, and 30 GLCH features differed dependent on bodyweight ratio. Contrary to conventional thermal features, the texture heterogeneity measures, InvDefMom, SumEntrp, Entropy, DifVarnc, and DifEntrp, expressed consistent measurable differences when the red component was considered.

Physical aspects of magnetized Jeffrey nanomaterial flow with irreversibility analysis

This analysis presents the applications of entropy generation phenomenon in incompressible flow of Jeffrey nanofluid in presence of distinct thermal features. The novel aspects of various features like Joule heating, porous medium, dissipation features and radiative mechanism is addressed. In order to improve the thermal transportation systems based on nanomaterials, the convective boundary conditions are introduced. The thermal viscoelastic nanofluid model is expressed in term of differential equations. The problem is presented via nonlinear differential equations for which analytical expressions are obtained by using homotopy analysis method(HAM). The accuracy of solution is ensured. The effective outcomes of all physical parameters associated with the flow model are carefully examined and underlined through various curves. The observations summarized from current analysis reveal that presence of permeability parameter offers resistance to the flow. A monotonic decrement in local Nusselt number is noted with Hartmann number and Prandtl number. Moreover, entropy generation and Bejan number increases with radiation parameter and fluid parameter.

Concentrated Thermomics for Early Diagnosis of Breast Cancer

Thermography has been employed broadly as a corresponding diagnostic instrument in breast cancer diagnosis. Among thermographic techniques, deep neural networks show an unequivocal potential to detect heterogeneous thermal patterns related to vasodilation in breast cancer cases. Such methods are used to extract high-dimensional thermal features, known as deep thermomics. In this study, we applied convex non-negative matrix factorization (convex NMF) to extract three predominant bases of thermal sequences. Then, the data were fed into a sparse autoencoder model, known as SPAER, to extract low-dimensional deep thermomics, which were then used to assist the clinical breast exam (CBE) in breast cancer screening. The application of convex NMF-SPAER, combining clinical and demographic covariates, yielded a result of 79.3% (73.5%, 86.9%); the highest result belonged to NMF-SPAER at 84.9% (79.3%, 88.7%). The proposed approach preserved thermal heterogeneity and led to early detection of breast cancer. It can be used as a noninvasive tool aiding CBE.

Polyaniline Synthesized by Different Dopants for Fluorene Detection via Photoluminescence Spectroscopy

The effects of different dopants on the synthesis, optical, electrical and thermal features of polyaniline were investigated. Polyaniline (PANI) doped with p-toluene sulfonic acid (PANI-PTSA), camphor sulphonic acid (PANI-CSA), acetic acid (PANI-acetic acid) and hydrochloric acid (PANI-HCl) was synthesized through the oxidative chemical polymerization of aniline under acidic conditions at ambient temperature. Fourier transform infrared light, X-ray diffraction, UV-visible spectroscopy, field emission scanning electron microscopy, photoluminescence spectroscopy and electrical analysis were used to define physical and structural features, bandgap values, electrical conductivity and type and degree of doping, respectively. Tauc calculation reveals the optical band gaps of PANI-PTSA, PANI-CSA, PANI-acetic acid and PANI-HCl at 3.1, 3.5, 3.6 and 3.9 eV, respectively. With the increase in dopant size, crystallinity is reduced, and interchain separations and d-spacing are strengthened. The estimated conductivity values of PANI-PTSA, PANI-CSA, PANI-acetic acid and PANI-HCl are 3.84 × 101, 2.92 × 101, 2.50 × 10−2, and 2.44 × 10−2 S·cm−1, respectively. Particularly, PANI-PTSA shows high PL intensity because of its orderly arranged benzenoid and quinoid units. Owing to its excellent synthesis, low bandgap, high photoluminescence intensity and high electrical features, PANI-PTSA is a suitable candidate to improve PANI properties and electron provider for fluorene-detecting sensors with a linear range of 0.001–10 μM and detection limit of 0.26 nM.