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The thermal theory of opposed-flow flame spread assumes that the fuel pyrolyzes at a constant pyrolysis temperature. However, the pyrolysis temperature cannot be thermodynamically determined, and its value is usually assigned from limited amount of experimental data that are available for different fuels. Detailed numerical modeling which employs finite-rate pyrolysis kinetics indicate that the pyrolysis temperature is not a constant across the pyrolysis zone and its value can also depend on several parameters such as the opposed-flow velocity, fuel thickness, etc. This situation is more pronounced for thermally thin solids where steady state conditions at a constant pyrolysis temperature are not achieved. In this work, a simplified scale analysis is performed to understand the behavior and dependence of the pyrolysis temperature and pyrolysis length on the fuel and environmental conditions. Two non-dimensional numbers - a pyrolysis number and a radiation number – are identified in the energy balance relation that control a competing behavior on the pyrolysis temperature. A closed-form formula for the pyrolysis temperature is developed through this analysis in the thermal regime that explains and qualitatively predicts the parametric behavior of the pyrolysis temperature when compared to a comprehensive numerical model and available experimental results. The agreement of the simplified model with the numerical solution can be improved by more properly quantifying the magnitude of the flame heat flux in the pyrolysing region. [ABSTRACT FROM AUTHOR]

Opposed-Flow flame spread over flat condensed fuel has been studied for many decades as it constitutes a fundamental problem of fire studies. Although cylindrical fuels offer the simplicity of axial symmetry, despite many previous attempts, there are no easy-to-use flame spread rate expressions that connect spread rate in this geometry to the exact solution for flame spread rate for flat fuels. Also no criterion exists to delineate a thermally thin cylindrical fuel from a thermally thick one. In this work, we use the scaling approach to develop closed-form expressions for flame spread rate for opposed-flow flame spread over thermally thin and thick cylindrical fuels. The approach is validated by reproducing the theoretical expressions of de Ris for flat fuels. By equating the heated layer thickness with the radius, a criterion is developed for delineating thermally thin cylinders from thermally thick ones. It is shown that the spread rate is always higher for a cylindrical fuel when compared to a flat fuel with a half-thickness equal to the radius of the cylindrical sample. The cylindrical spread rate depends on the same factors as that of flat fuels with an additional coefficient arising out of heat transfer enhanced by the curvature effect. For thermally thin cylinders, the spread rate is predicted to be at least two times faster than the corresponding flat fuel. For thermally thick fuels also the spread rate is significantly higher, but as the radius is increased the flat fuel limit is approached. Spread rates obtained from experiments performed with PMMA cylinders of different diameters in downward configuration are shown to agree well with the theoretical expressions. The thermally thick limit for downward spread, interestingly, is achieved for PMMA cylinders as narrow as 2.4 mm diameter. [ABSTRACT FROM AUTHOR]

The effect of the internal angle on downward flame spread over a thick folded plate is experimentally investigated in this study. The lateral side of a thermoplastic plate is cut at desired angles and two of these plates are welded to form a single folded sample. All the faces other than the front and two 5-mm areas of the front face from each side were coated to inhibit the combustion. Experiments are conducted for various folded plates of different angles, 60°, 90°, 120°, and 180°. Following ignition at the upper edge of the sample, the opposed flame spread on the front face is observed and the flame spread rate at various positions is quantified by image analysis. The flame spreads more rapidly at the folding edge, while the other parts then accelerate to catch up with the corner spread and, eventually, all the parts across the width achieve the same flame spread rate as the folding edge. This result indicates that the flame spread rate at the folding edge is the characteristic value which represents the steady-state of the system of interest. A simple model is developed considering change of heated volume against the flame and change of the induced flow velocity due to the geometry. The former effect is formulated from geometrical consideration in the vicinity of the edge, while the latter is formulated based on an experimental fact that the flame height is inversely proportional to the internal angle. A prediction formula of the flame spread rate at the folding edge is established by modifying the conventional one for the flat plate. The calculated flame spread rate shows reasonably good agreement with the experimental data. This study helps fundamental understanding of the flame spread behavior of practical combustibles such as pillars or rods with various cross-sectional shapes. [ABSTRACT FROM AUTHOR]

Introduction: Automatic nuclear segmentation in digital microscopic tissue images can aid pathologists to extract high-quality features for nuclear morphometrics and other analyses. However, image segmentation is a challenging task in medical image processing and analysis. This study aimed to develop a deep learning-based method for nuclei segmentation of histological images for computational pathology. Methods: The original U-Net model sometime has a caveat in exploring significant features. Herein, we present the Densely Convolutional Spatial Attention Network (DCSA-Net) model based on U-Net to perform the segmentation task. Furthermore, the developed model was tested on external multi-tissue dataset -- MoNuSeg. To develop deep learning algorithms for wellsegmenting nuclei, a large quantity of data are mandatory, which is expensive and less feasible. We collected hematoxylin and eosin--stained image data sets from two hospitals to train the model with a variety of nuclear appearances. Because of the limited number of annotated pathology images, we introduced a small publicly accessible data set of prostate cancer (PCa) with more than 16,000 labeled nuclei. Nevertheless, to construct our proposed model, we developed the DCSA module, an attention mechanism for capturing useful information from raw images. We also used several other artificial intelligence-based segmentation methods and tools to compare their results to our proposed technique. Results: To prioritize the performance of nuclei segmentation, we evaluated the model's outputs based on the Accuracy, Dice coefficient (DC), and Jaccard coefficient (JC) scores. The proposed technique outperformed the other methods and achieved superior nuclei segmentation with accuracy, DC, and JC of 96.4% (95% confidence interval [CI]: 96.2 -- 96.6), 81.8 (95% CI: 80.8 -- 83.0), and 69.3 (95% CI: 68.2 -- 70.0), respectively, on the internal test data set. Conclusion: Our proposed method demonstrates superior performance in segmenting cell nuclei of histological images from internal and external datasets, and outperforms many standard segmentation algorithms used for comparative analysis. [ABSTRACT FROM AUTHOR]

The novel coronavirus (COVID-19), which emerged as a pandemic, has engulfed so many lives and affected millions of people across the world since December 2019. Although this disease is under control nowadays, yet it is still affecting people in many countries. The traditional way of diagnosis is time taking, less efficient, and has a low rate of detection of this disease. Therefore, there is a need for an automatic system that expedites the diagnosis process while retaining its performance and accuracy. Artificial intelligence (AI) technologies such as machine learning (ML) and deep learning (DL) potentially provide powerful solutions to address this problem. In this study, a state-of-the-art CNN model densely connected squeeze convolutional neural network (DCSCNN) has been developed for the classification of X-ray images of COVID-19, pneumonia, normal, and lung opacity patients. Data were collected from different sources. We applied different preprocessing techniques to enhance the quality of images so that our model could learn accurately and give optimal performance. Moreover, the attention regions and decisions of the AI model were visualized using the Grad-CAM and LIME methods. The DCSCNN combines the strength of the Dense and Squeeze networks. In our experiment, seven kinds of classification have been performed, in which six are binary classifications (COVID vs. normal, COVID vs. lung opacity, lung opacity vs. normal, COVID vs. pneumonia, pneumonia vs. lung opacity, pneumonia vs. normal) and one is multiclass classification (COVID vs. pneumonia vs. lung opacity vs. normal). The main contributions of this paper are as follows. First, the development of the DCSNN model which is capable of performing binary classification as well as multiclass classification with excellent classification accuracy. Second, to ensure trust, transparency, and explainability of the model, we applied two popular Explainable AI techniques (XAI). i.e., Grad-CAM and LIME. These techniques helped to address the black-box nature of the model while improving the trust, transparency, and explainability of the model. Our proposed DCSCNN model achieved an accuracy of 98.8% for the classification of COVID-19 vs normal, followed by COVID-19 vs. lung opacity: 98.2%, lung opacity vs. normal: 97.2%, COVID-19 vs. pneumonia: 96.4%, pneumonia vs. lung opacity: 95.8%, pneumonia vs. normal: 97.4%, and lastly for multiclass classification of all the four classes i.e., COVID vs. pneumonia vs. lung opacity vs. normal: 94.7%, respectively. The DCSCNN model provides excellent classification performance consequently, helping doctors to diagnose diseases quickly and efficiently. [ABSTRACT FROM AUTHOR]

Opposed-flow flame spread over solid fuels is an important case of fire safety studies. Spread rate and flame structure depend on fuel nature and burning conditions, and they can be used to study material flammability and fundamental combustion mechanisms. Previous studies provided important insights on flames developing over vertical walls and under the effect of a forced flow. However, there are limited works available in literature on the prediction of flame length of spreading flames even though it is the main driver of flame growth in concurrent-flow flame spread. In this work we consider the dependence of flame length on fuel thickness and flow velocity. Acrylic samples, with thicknesses from 0.05 to 12 mm, are burnt vertically in a wind tunnel against flow velocities between 0 and 200 cm/s. Without a forced flow, the flame length strongly depends on fuel thickness. Using scaling theory, closed-form expressions for both opposed-flow and downward spreading flames are developed and agree qualitatively with the data. For downward spread, the developed expression is shown to be quite similar to the theoretical flame length expression for gaseous diffusion flame by Roper. A least-squares fit of the developed expression with the data produces the undetermined constant of the scaled expression, producing a formula from which flame length over solid fuels (with both sides burning) can be predicted with a 90% accuracy. [ABSTRACT FROM AUTHOR]