Breaking the symmetry of a wavy channel alters the route to chaotic flow.

We numerically explore the two-dimensional, incompressible, isothermal flow through a wavy channel, with a focus on how the channel geometry affects the routes to chaos at Reynolds numbers between 150 and 1000. We find that (i) the period-doubling route arises in a symmetric channel, (ii) the Ruelle-Takens-Newhouse route arises in an asymmetric channel, and (iii) the type-II intermittency route arises in both asymmetric and semiwavy channels. We also find that the flow through the semiwavy channel evolves from a quasiperiodic torus to an unstable invariant set (chaotic saddle), before eventually settling on a period-1 limit-cycle attractor. This study reveals that laminar channel flow at elevated Reynolds numbers can exhibit a variety of nonlinear dynamics. Specifically, it highlights how breaking the symmetry of a wavy channel can not only influence the critical Reynolds number at which chaos emerges, but also diversify the types of bifurcation encountered en route to chaos itself.

Introduction to Advances in Sustainable Hydrogen Energy.

Nader Karimi, Larry K. B. Li, Manosh C. Paul, Mohammad Hossein Doranehgard and Freshteh Sotoudeh introduce the RSC Advances themed issue on Advances in Sustainable Hydrogen Energy.

Recent progress on flat plate solar collectors equipped with nanofluid and turbulator: state of the art.

This paper reviews the impacts of employing inserts, nanofluids, and their combinations on the thermal performance of flat plate solar collectors. The present work outlines the new studies on this specific kind of solar collector. In particular, the influential factors upon operation of flat plate solar collectors with nanofluids are investigated. These include the type of nanoparticle, kind of base fluid, volume fraction of nanoparticles, and thermal efficiency. According to the reports, most of the employed nanofluids in the flat plate solar collectors include Al2O3, CuO, and TiO2. Moreover, 62.34%, 16.88%, and 11.26% of the utilized nanofluids have volume fractions between 0 and 0.5%, 0.5 and 1%, and 1 and 2%, respectively. The twisted tape is the most widely employed of various inserts, with a share of about one-third. Furthermore, the highest achieved flat plate solar collectors' thermal efficiency with turbulator is about 86.5%. The review is closed with a discussion about the recent analyses on the simultaneous use of nanofluids and various inserts in flat plate solar collectors. According to the review of works containing nanofluid and turbulator, it has been determined that the maximum efficiency of about 84.85% can be obtained from a flat plate solar collector. It has also been observed that very few works have been done on the combination of two methods of employing nanofluid and turbulator in the flat plate solar collector, and more detailed work can still be done, using more diverse nanofluids (both single and hybrid types) and turbulators with more efficient geometries.

Predicting the effects of environmental parameters on the spatio-temporal distribution of the droplets carrying coronavirus in public transport - A machine learning approach.

Human-generated droplets constitute the main route for the transmission of coronavirus. However, the details of such transmission in enclosed environments are yet to be understood. This is because geometrical and environmental parameters can immensely complicate the problem and turn the conventional analyses inefficient. As a remedy, this work develops a predictive tool based on computational fluid dynamics and machine learning to examine the distribution of sneezing droplets in realistic configurations. The time-dependent effects of environmental parameters, including temperature, humidity and ventilation rate, upon the droplets with diameters between 1 and 250 µ m are investigated inside a bus. It is shown that humidity can profoundly affect the droplets distribution, such that 10% increase in relative humidity results in 30% increase in the droplets density at the farthest point from a sneezing passenger. Further, ventilation process is found to feature dual effects on the droplets distribution. Simple increases in the ventilation rate may accelerate the droplets transmission. However, carefully tailored injection of fresh air enhances deposition of droplets on the surfaces and thus reduces their concentration in the bus. Finally, the analysis identifies an optimal range of temperature, humidity and ventilation rate to maintain human comfort while minimising the transmission of droplets.

Classification of diabetic retinopathy using unlabeled data and knowledge distillation.

Over the last decade, advances in Machine Learning and Artificial Intelligence have highlighted their potential as a diagnostic tool in the healthcare domain. Despite the widespread availability of medical images, their usefulness is severely hampered by a lack of access to labeled data. For example, while Convolutional Neural Networks (CNNs) have emerged as an essential analytical tool in image processing, their impact is curtailed by training limitations due to insufficient labeled data availability. Transfer Learning enables models developed for one task to be reused for a second task. Knowledge distillation enables transferring knowledge from a pre-trained model to another. However, it suffers from limitations, and the two models' constraints need to be architecturally similar. Knowledge distillation addresses some of the shortcomings of transfer learning by generalizing a complex model to a lighter model. However, some parts of the knowledge may not be distilled by knowledge distillation sufficiently. In this paper, a novel knowledge distillation approach using transfer learning is proposed. The proposed approach transfers the complete knowledge of a model to a new smaller one. Unlabeled data are used in an unsupervised manner to transfer the new smaller model's maximum amount of knowledge. The proposed method can be beneficial in medical image analysis, where labeled data are typically scarce. The proposed approach is evaluated in classifying images for diagnosing Diabetic Retinopathy on two publicly available datasets, including Messidor and EyePACS. Simulation results demonstrate that the approach effectively transfers knowledge from a complex model to a lighter one. Furthermore, experimental results illustrate that different small models' performance is improved significantly using unlabeled data and knowledge distillation.

On the Response of Ultralean Combustion of CH4/H2 Blends in a Porous Burner to Fluctuations in Fuel Flow-an Experimental Investigation.

Fluctuations in the fuel flow rate may occur in practical combustion systems and result in flame destabilization. This is particularly problematic in lean and ultralean modes of burner operation. In this study, the response of a ceramic porous burner to fluctuations in the flow rate of different blends of methane and hydrogen is investigated experimentally. Prior to injection into the porous burner, the fuel blend is premixed with air at equivalence ratios below 0.275. The fuel streams are measured and controlled separately by programmable mass flow controllers, which impose sinusoidal fluctuations on the flow rates. To replicate realistic fluctuations in the fuel flow rate, the period of oscillations is chosen to be on the order of minutes. The temperature inside the ceramic foam is measured using five thermocouples located at the center of the working section of the burner. The flame embedded in porous media is imaged while the fuel flow is modulated. Analysis of the flame pictures and temperature traces shows that the forced oscillation of the fuel mixture leads to flame movement within the burner. This movement is found to act in accordance with the fluctuations in methane and hydrogen flows for both CH4(90%)-H2(10%) and CH4(70%)-H2(30%) mixtures. However, both fuel mixtures are noted to be rather insensitive to hydrogen flow fluctuation with a modulation amplitude below 30% of the steady flow. For the CH4(70%)-H2(30%) mixture, the flame in the porous medium can be modulated by fluctuations between 0 and 30% of steady methane flow without any noticeable flame destabilization.

Detection of COVID-19 in X-ray images by classification of bag of visual words using neural networks.

Coronavirus disease 2019 (COVID-19) was classified as a pandemic by the World Health Organization in March 2020. Given that this novel virus most notably affects the human respiratory system, early detection may help prevent severe lung damage, save lives, and help prevent further disease spread. Given the constraints on the healthcare facilities and staff, the role of artificial intelligence for automatic diagnosis is critical. The automatic diagnosis of COVID-19 based on medical images is, however, not straightforward. Due to the novelty of the disease, available X-ray datasets are very limited. Furthermore, there is a significant similarity between COVID-19 X-rays and other lung infections. In this paper, these challenges are addressed by proposing an approach consisting of a bag of visual words and a neural network classifier. The proposed method can classify X-ray chest images into non-COVID-19 and COVID-19 with high performance. Three public datasets are used to evaluate the proposed approach. Our best accuracy on the first, second, and third datasets is 96.1, 99.84, and 98 percent. Since detection of COVID-19 is important, sensitivity is used as a criterion. The proposed method's best sensitivities are 90.32, 99.65, and 91 percent on these datasets, respectively. The experimental results show that extracting features with the bag of visual words results in better classification accuracy than the state-of-the-art techniques.

Prediction of the spread of Corona-virus carrying droplets in a bus - A computational based artificial intelligence approach.

Public transport has been identified as high risk as the corona-virus carrying droplets generated by the infected passengers could be distributed to other passengers. Therefore, predicting the patterns of droplet spreading in public transport environment is of primary importance. This paper puts forward a novel computational and artificial intelligence (AI) framework for fast prediction of the spread of droplets produced by a sneezing passenger in a bus. The formation of droplets of salvia is numerically modelled using a volume of fluid methodology applied to the mouth and lips of an infected person during the sneezing process. This is followed by a large eddy simulation of the resultant two phase flow in the vicinity of the person while the effects of droplet evaporation and ventilation in the bus are considered. The results are subsequently fed to an AI tool that employs deep learning to predict the distribution of droplets in the entire volume of the bus. This combined framework is two orders of magnitude faster than the pure computational approach. It is shown that the droplets with diameters less than 250 micrometers are most responsible for the transmission of the virus, as they can travel the entire length of the bus.

Dense pooling layers in fully convolutional network for skin lesion segmentation.

One of the essential tasks in medical image analysis is segmentation and accurate detection of borders. Lesion segmentation in skin images is an essential step in the computerized detection of skin cancer. However, many of the state-of-the-art segmentation methods have deficiencies in their border detection phase. In this paper, a new class of fully convolutional network is proposed, with new dense pooling layers for segmentation of lesion regions in skin images. This network leads to highly accurate segmentation of lesions on skin lesion datasets, which outperforms state-of-the-art algorithms in the skin lesion segmentation.

Hierarchical Pruning for Simplification of Convolutional Neural Networks in Diabetic Retinopathy Classification.

Convolutional neural networks (CNNs) are widely used in automatic detection and analysis of diabetic retinopathy (DR). Although CNNs have proper detection performance, their structural and computational complexity is troublesome. In this study, the problem of reducing CNN's structural complexity for DR analysis is addressed by proposing a hierarchical pruning method. The original VGG16-Net is modified to have fewer parameters and is employed for DR classification. To have an appropriate feature extraction, pre-trained model parameters on Image-Net dataset are used. Hierarchical pruning gradually eliminates the connections, filter channels, and filters to simplify the network structure. The proposed pruning method is evaluated using the Messidor image dataset which is a public dataset for DR classification. Simulation results show that by applying the proposed simplification method, 35% of the feature maps are pruned resulting in only 1.89% accuracy drop. This simplification could make CNN suitable for implementation inside medical diagnostic devices.

Gland Segmentation in Histopathology Images Using Deep Networks and Handcrafted Features.

Histopathology images contain essential information for medical diagnosis and prognosis of cancerous disease. Segmentation of glands in histopathology images is a primary step for analysis and diagnosis of an unhealthy patient. Due to the widespread application and the great success of deep neural networks in intelligent medical diagnosis and histopathology, we propose a modified version of LinkNet for gland segmentation and recognition of malignant cases. We show that using specific handcrafted features such as invariant local binary pattern drastically improves the system performance. The experimental results demonstrate the competency of the proposed system against the state-of-the-art methods. We achieved the best results in testing on section B images of the Warwick-QU dataset and obtained comparable results on section A images.

Liver Segmentation in Abdominal CT Images Using Probabilistic Atlas and Adaptive 3D Region Growing.

Automatic liver segmentation plays a vital role in computer-aided diagnosis or treatment. Manual segmentation of organs is a tedious and challenging task and is prone to human errors. In this paper, we propose innovative pre-processing and adaptive 3D region growing methods with subject-specific conditions. To obtain strong edges and high contrast, we propose effective contrast enhancement algorithm then we use the atlas intensity distribution of most probable voxels in probability maps along with location before designing conditions for our 3D region growing method. We also incorporate the organ boundary to restrict the region growing. We compare our method with the label fusion of 13 organs on state-of-the-art Deeds registration method and achieved Dice score of 92.56%.

Fetal Ultrasound Image Segmentation for Measuring Biometric Parameters Using Multi-Task Deep Learning.

Ultrasound imaging is a standard examination during pregnancy that can be used for measuring specific biometric parameters towards prenatal diagnosis and estimating gestational age. Fetal head circumference (HC) is one of the significant factors to determine the fetus growth and health. In this paper, a multi-task deep convolutional neural network is proposed for automatic segmentation and estimation of HC ellipse by minimizing a compound cost function composed of segmentation dice score and MSE of ellipse parameters. Experimental results on fetus ultrasound dataset in different trimesters of pregnancy show that the segmentation results and the extracted HC match well with the radiologist annotations. The obtained dice scores of the fetal head segmentation and the accuracy of HC evaluations are comparable to the state-of-the-art.

Deep Neural Network based Polyp Segmentation in Colonoscopy Images using a Combination of Color Spaces.

Colorectal cancer (CRC) is the second leading cause of cancer death. Colorectal polyps cause most colorectal cancer cases. Colonoscopy is considered as the most common method for diagnosis of colorectal polyps, and early detection and segmentation of them can prevent colorectal cancer. On the other hand, today advances in computer systems persuade researchers all around the world to use computer-aided systems to help physicians in their diagnosis. Many modern types of researches and methods have proposed for this goal, and we have aggregated the methods based on previous convolutional neural networks with more recent networks in this paper to improve the quality of segmentation. We also chose the red channel, green channel and the b* component of CIE-L*a*b* as the input of network to leverage the parameters of segmentation result such as dice and sensitivity. LinkNet is used as the convolutional network, and the results show that it is suitable for segmentation. Performance of our method is evaluated on CVC-ColonDB. The results show that our method outperforms previous works in colorectal polyp segmentation field.

Low Complexity CNN Structure for Automatic Bleeding Zone Detection in Wireless Capsule Endoscopy Imaging.

Wireless capsule endoscopy (WCE) is a swallowable device used for screening different parts of the human digestive system. Automatic WCE image analysis methods reduce the duration of the screening procedure and alleviate the burden of manual screening by medical experts. Recent studies widely employ convolutional neural networks (CNNs) for automatic analysis of WCE images; however, these studies do not consider CNN's structural and computational complexities. In this paper, we address the problem of simplifying the CNN's structure. A low complexity CNN structure for bleeding zone detection is proposed which takes a single patch as input and then outputs a segmented patch of the same size. The proposed network is inspired by the FCN paradigm with a simplified structure. Since it is based on image patches, the resulting network benefits from moderate-sized intermediate feature maps. Moreover, the problem of redundant computations in patch-based methods is circumvented by non-overlapping patch processing. The proposed method is evaluated using the publicly available KID dataset for WCE image analysis. Experimental results show that the proposed network has better accuracy and AUC than previous structures while requiring less computational operations.

Non-Equilibrium Thermodynamics of Micro Technologies.

This is the Editorial article summarizing the scope and contents of the Special Issue, Non-Equilibrium Thermodynamics of Micro Technologies.

Lossless Compression of Angiogram Foreground with Visual Quality Preservation of Background.

By increasing the volume of telemedicine information, the need for medical image compression has become more important. In angiographic images, a small ratio of the entire image usually belongs to the vasculature that provides crucial information for diagnosis. Other parts of the image are diagnostically less important and can be compressed with higher compression ratio. However, the quality of those parts affects the overall understanding of the image as well. Existing methods compress foreground and background of angiographic images using different techniques. In this paper, we first utilize a convolutional neural network to segment vessels and then represent a hierarchical block processing algorithm capable of both eliminating the background redundancies and preserving the overall visual quality of angiograms.

Classification of Informative Frames in Colonoscopy Videos Using Convolutional Neural Networks with Binarized Weights.

Colorectal cancer is one of the common cancers in the United States. Polyps are one of the major causes of colonic cancer, and early detection of polyps will increase the chance of cancer treatments. In this paper, we propose a novel classification of informative frames based on a convolutional neural network with binarized weights. The proposed CNN is trained with colonoscopy frames along with the labels of the frames as input data. We also used binarized weights and kernels to reduce the size of CNN and make it suitable for implementation in medical hardware. We evaluate our proposed method using Asu Mayo Test clinic database, which contains colonoscopy videos of different patients. Our proposed method reaches a dice score of 71.20% and accuracy of more than 90% using the mentioned dataset.

Polyp Segmentation in Colonoscopy Images Using Fully Convolutional Network.

Colorectal cancer is one of the highest causes of cancer-related death, especially in men. Polyps are one of the main causes of colorectal cancer, and early diagnosis of polyps by colonoscopy could result in successful treatment. Diagnosis of polyps in colonoscopy videos is a challenging task due to variations in the size and shape of polyps. In this paper, we proposed a polyp segmentation method based on the convolutional neural network. Two strategies enhance the performance of the method. First, we perform a novel image patch selection method in the training phase of the network. Second, in the test phase, we perform effective post-processing on the probability map that is produced by the network. Evaluation of the proposed method using the CVC-ColonDB database shows that our proposed method achieves more accurate results in comparison with previous colonoscopy video-segmentation methods.

Reversible Image Watermarking for Health Informatics Systems Using Distortion Compensation in Wavelet Domain.

Reversible image watermarking guaranties restoration of both original cover and watermark logo from the watermarked image. Capacity and distortion of the image under reversible watermarking are two important parameters. In this study, a reversible watermarking is investigated by focusing on increasing the embedding capacity and reducing the distortion in medical images. We use integer wavelet transform for embedding one bit of watermark in a transform coefficient. We devise a novel approach that when a coefficient is modified in one iteration, the produced distortion is compensated in the next iteration. This distortion compensation method would result in low distortion rate. The proposed method is tested on four types of medical images including MRI of the brain, cardiac MRI, MRI of breast and intestinal polyp images. The maximum capacity of 1.5 BPP is obtained by using a one-level wavelet transform. Experimental results demonstrate that the proposed method is superior to the stateof-the-art works in terms of capacity and distortion.