Development of methodology for detection of abnormal functioning region in human external organs using thermogram analysis

This study has established thermography as a potential diagnostic tool for detecting and parameterizing tumors even at the earlier stage from abnormal local surface thermal features captured by high sensitive infrared cameras without known risk of morbidity. Discrepant thermal features originate not only for tumor’s distinguishing physio-bio-thermal features but also for the state of health, resulting in thermography as a useful tool for retrieving information about the tissue’s health, thus an efficient screening tool. Accurate linking between hyper functional tissues and thermal pattern could turn the screening tool into a promising parameterizing tool. Human external organs, for example chest, forearm and breast have been modeled, mimicking their shapes, inhomogeneity and deformations to obtain steady-state thermal feature in the tissue interior at healthy state and the computation is extended for tumors buried into healthy tissues for determining abnormal local surface thermal image. Tumor diagnosis parameters have been forecasted from thermogram using an indirect process involving the optimization process. The study has applied gradient (gradient projection method), non-gradient (pattern search method) and learning based (genetic algorithm) optimization approaches. Feasibility of the proposed technique is investigated for tumors in human organs. The local abnormal thermal feature screens earlier stage tumors out and reveal how tumors affect the thermal behaviour and what particular parameters have significant influence on the thermal image. Influential parameters are applied as optimization variables and their influences are also figured out to determine the gradient matrix for the gradient optimization technique. The study has employed bio-heat equations, heat-source model and Artificial Neural Network as governing equation to develop simulated datasets. The simulated dataset is compared with test thermogram to minimize a cost function. In lieu of clinical thermograms, the study has developed pretend thermogram with enveloping the simulated datasets with ±10% random noise. This research has tailored optimization algorithms for estimating tumor depth, size, blood perfusion rate, thermal conductivity, and metabolism and the obtained results show good accuracy. The estimated parameters are given to a trained network to reconstruct the thermal feature, thus, validates the performance of the proposed methodology.

Development of methodology for detection of abnormal functioning region in human external organs using thermogram analysis

This study has established thermography as a potential diagnostic tool for detecting and parameterizing tumors even at the earlier stage from abnormal local surface thermal features captured by high sensitive infrared cameras without known risk of morbidity. Discrepant thermal features originate not only for tumor’s distinguishing physio-bio-thermal features but also for the state of health, resulting in thermography as a useful tool for retrieving information about the tissue’s health, thus an efficient screening tool. Accurate linking between hyper functional tissues and thermal pattern could turn the screening tool into a promising parameterizing tool. Human external organs, for example chest, forearm and breast have been modeled, mimicking their shapes, inhomogeneity and deformations to obtain steady-state thermal feature in the tissue interior at healthy state and the computation is extended for tumors buried into healthy tissues for determining abnormal local surface thermal image. Tumor diagnosis parameters have been forecasted from thermogram using an indirect process involving the optimization process. The study has applied gradient (gradient projection method), non-gradient (pattern search method) and learning based (genetic algorithm) optimization approaches. Feasibility of the proposed technique is investigated for tumors in human organs. The local abnormal thermal feature screens earlier stage tumors out and reveal how tumors affect the thermal behaviour and what particular parameters have significant influence on the thermal image. Influential parameters are applied as optimization variables and their influences are also figured out to determine the gradient matrix for the gradient optimization technique. The study has employed bio-heat equations, heat-source model and Artificial Neural Network as governing equation to develop simulated datasets. The simulated dataset is compared with test thermogram to minimize a cost function. In lieu of clinical thermograms, the study has developed pretend thermogram with enveloping the simulated datasets with ±10% random noise. This research has tailored optimization algorithms for estimating tumor depth, size, blood perfusion rate, thermal conductivity, and metabolism and the obtained results show good accuracy. The estimated parameters are given to a trained network to reconstruct the thermal feature, thus, validates the performance of the proposed methodology.

Increased density of large low-velocity provinces recovered by seismologically constrained gravity inversion

The nature and origin of the two large low-velocity provinces (LLVPs) in the lowest part of the mantle remain controversial. These structures have been interpreted as a purely thermal feature, accumulation of subducted oceanic lithosphere or a primordial zone of iron enrichment. Information regarding the density of the LLVPs would help to constrain a possible explanation. In this work, we perform a density inversion for the entire mantle, by constraining the geometry of potential density anomalies using tomographic vote maps. Vote maps describe the geometry of potential density anomalies according to their agreement with multiple seismic tomographies, hence not depending on a single representation. We use linear inversion and determine the regularization parameters using cross-validation. Two different input fields are used to study the sensitivity of the mantle density results to the treatment of the lithosphere. We find the best data fit is achieved if we assume that the lithosphere is in isostatic balance. The estimated densities obtained for the LLVPs are systematically positive density anomalies for the LLVPs in the lower 800–1000 km of the mantle, which would indicate a chemical component for the origin of the LLVPs. Both iron-enrichment and a mid-oceanic ridge basalt (MORB) contribution are in accordance with our data, but the required superadiabatic temperature anomalies for MORB would be close to 1000 K.

Thermal investigation of fully wet longitudinal porous fin of functionally graded material

Purpose The purpose of this study is to conduct a numerical computation to analyse the thermal attribute and heat transfer phenomenon of a fully wetted porous fin of a longitudinal profile. The fin considered is that of a functionally graded material (FGM). Based on the spatial dependency of thermal conductivity, three cases such as linear, quadratic and exponential FGMs are analysed. Design/methodology/approach The governing equations are nondimensionalised and solved by applying Runge-Kutta-Fehlberg fourth-fifth order technique. Findings The parametric investigation is executed to access the significance of the pertinent parameters on the thermal feature of the fin and heat transmit rate. The outcomes are portrayed in a graphical form. Originality/value No such study has yet been published in the literature.

Increased LLVP density recovered by seismologically constrained gravity inversion

The nature and origin of the two Large Low Velocity Provinces in the lowest part of the mantle remain controversial. They have been interpreted as a purely thermal feature, accumulation of subducted oceanic lithosphere or a primordial zone of iron enrichment. Information regarding the density of the LLVPs would help to constrain a possible explanation. In this work, we perform a density inversion for the entire mantle, by constraining the geometry of potential density anomalies using tomographic vote maps. Vote maps describe the geometry of potential density anomalies according to their agreement of multiple seismic tomographies, hence not depending on a single representation. Therefore, the geometries used for inversion are features observed in most tomographies. We use linear inversion and determine the regularization parameters using cross-validation. Two different input fields are used to study the sensitivity of the mantle density results to the treatment of the lithosphere. We find the best data fit is achieved if we assume that the lithosphere is in isostatic balance. The estimated densities obtained for the LLVPs are systematically positive density anomalies for the LLVPs in the lower 800–1000 km of the mantle, which would indicate a chemical component for the origin of the LLVPs. Both iron-enrichment and a MORB contribution are in accordance with our data, but the required super-adiabatic temperature anomalies for MORB would be close to 1000 K.

Transient nanofluid squeezing cooling process using aluminum oxide nanoparticle

Computational studies have been widely applied for the thermal evaluation of the nanomaterial thermal feature in different industrial and scientific issues. The squeezed flow and heat transfer features for Al2O3-water nanofluid among analogous plates are investigated using the GOHAM and its validity is verified by comparison with existing numerical results. Novel aspects of Brownian motion and thermal force were accounted in the simulation of nanomaterial flow within parallel plate. Analytical investigation has been done for diverse governing factors namely: the squeeze, chemical reaction factors and Eckert number. The obtained outcomes show that [Formula: see text] has direct relationship with absolute values of squeeze factor. Nu increases for large Eckert number and absolute values of squeeze number.