Study of thermal behavior of a biological tissue: an equivalence of Pennes bioheat equation and Wulff continuum model

Predictive Models of Dairy Cow Thermal State: A Review from a Technological Perspective

Simple Summary

Heat stress in cattle is broadly defined as a physiological condition in which body temperature rises, and the animals are no longer able to adequately dissipate body heat to maintain thermal equilibrium due to environmental factors. Dairy cattle are particularly sensitive to heat stress because of the higher metabolic rate needed for milk production. Due to global warming and the expected growth of milk production in warmer regions, an increase in the occurrence of heat stress can only be avoided with the use of environmental control systems. However, most available systems were developed to take corrective measures or are not accurate enough to effectively prevent heat stress, as there is not yet an automated technological solution that considers all the environmental and animal variables that determine the occurrence of this condition. Further, these systems must be connected in time to prevent this condition in cattle but also disconnected when they are no longer needed, as their use raises major economic and environmental concerns regarding energy and water consumption. This review describes and discusses three types of predictive models that can make these systems more effective in preventing heat stress and more efficient in the use of energy and water.

Abstract

Dairy cattle are particularly sensitive to heat stress due to the higher metabolic rate needed for milk production. In recent decades, global warming and the increase in dairy production in warmer countries have stimulated the development of a wide range of environmental control systems for dairy farms. Despite their proven effectiveness, the associated energy and water consumption can compromise the viability of dairy farms in many regions, due to the cost and scarcity of these resources. To make these systems more efficient, they should be activated in time to prevent thermal stress and switched off when that risk no longer exists, which must consider environmental variables as well as the variables of the animals themselves. Nowadays, there is a wide range of sensors and equipment that support farm routine procedures, and it is possible to measure several variables that, with the aid of algorithms based on predictive models, would allow anticipating animals’ thermal states. This review summarizes three types of approaches as predictive models: bioclimatic indexes, machine learning, and mechanistic models. It also focuses on the application of the current knowledge as algorithms to be used in the management of diverse types of environmental control systems.

Towards an Accurate MRI Acute Ischemic Stroke Lesion Segmentation Based on Bioheat Equation and U-Net Model

Acute ischemic stroke represents a cerebrovascular disease, for which it is practical, albeit challenging to segment and differentiate infarct core from salvageable penumbra brain tissue. Ischemic stroke causes the variation of cerebral blood flow and heat generation due to metabolism. Therefore, the temperature is modified in the ischemic stroke region. In this paper, we incorporate acute ischemic stroke temperature profile to reinforce segmentation accuracy in MRI. Pennes bioheat equation was used to generate brain thermal images that may provide rich information regarding the temperature change in acute ischemic stroke lesions. The thermal images were generated by calculating the temperature of the brain with acute ischemic stroke. Then, U-Net was used in this paper for the segmentation of acute ischemic stroke. A dataset of 3192 images was created to train U-Net using k-fold crossvalidation. The training time was about 10 hours and 35 minutes in NVIDIA GPU. Next, the obtained trained model was compared with recent methods to analyze the effect of the ischemic stroke temperature profile in segmentation. The obtained results show that significant parts of acute ischemic stroke and background areas are segmented only in thermal images, which proves the importance of using thermal information to improve the segmentation outcomes in MRI diagnosis.

Determining the thermal characteristics of breast cancer based on high-resolution infrared imaging, 3D breast scans, and magnetic resonance imaging

For over the three decades, various researchers have aimed to construct a thermal (or bioheat) model of breast cancer, but these models have mostly lacked clinical data. The present study developed a computational thermal model of breast cancer based on high-resolution infrared (IR) images, real three-dimensional (3D) breast surface geometries, and internal tumor definition of a female subject histologically diagnosed with breast cancer. A state-of-the-art IR camera recorded IR images of the subject’s breasts, a 3D scanner recorded surface geometries, and standard diagnostic imaging procedures provided tumor sizes and spatial locations within the breast. The study estimated the thermal characteristics of the subject’s triple negative breast cancer by calibrating the model to the subject’s clinical data. Constrained by empirical blood perfusion rates, metabolic heat generation rates reached as high as 2.0E04 W/m³ for normal breast tissue and ranged between 1.0E05–1.2E06 W/m³ for cancerous breast tissue. Results were specific to the subject’s unique breast cancer molecular subtype, stage, and lesion size and may be applicable to similar aggressive cases. Prior modeling efforts are briefly surveyed, clinical data collected are presented, and finally thermal modeling results are presented and discussed.

Relationship between metabolic rate and blood perfusion under Fanger thermal comfort conditions

The one-dimensional steady Pennes (bioheat) equation was applied to analyze heat conduction inside a combined layer of human muscle and fat, under Fanger thermal comfort conditions. The bioheat equation was solved subject to two boundary conditions at the skin surface: a prescribed skin temperature satisfying the Fanger comfort criterion, and a prescribed heat flux obtained from the overall energy balance for the system. In addition to a fixed body core temperature, an adiabatic condition was imposed as an auxiliary condition at the core of the body, and a pair of equations were derived, relating the blood perfusion and the volumetric heat generation rate for a given activity level and environmental conditions. By solving the two equations, we determined the functional dependence of blood perfusion and metabolic heat generation on the human activity level. For convenience, we presented simple explicit expressions for the key relations, with the aid of asymptotic analyses. Additional results include the temperature distribution inside the muscle layer, and the effects of muscle and fat layer thickness on the heat transfer processes.