Vibration Analysis of Rotating Fans Mounted on Adjacent Rectangular Foundation Blocks

Vibration analysis was conducted for large rotating fans mounted on adjacent rectangular concrete foundation blocks, 66 ft x 22 ft x 10 ft depth, with the adjacent long sides 10 ft apart. The blocks were embedded in medium dense sands and gravels with a variable shear-wave velocity profile. The purpose of the analysis was to determine whether (1) the dynamic interaction of the blocks through the surrounding soil would cause unacceptable vibratory response of the fans, and (2) the foundation stiffness criterion set by the vendor was satisfied. Solutions were obtained using the 3-D dynamic version of the FLAC computer program, which was first used to compute the response of a single block-fan system. The introduction of the second block-fan system into the model resulted in less than 10% amplification in dynamic response of the two-block system relative to the single block-fan response, when the excitation forces of both fans were in phase (i.e. 0° lag). However, a maximum amplification of 100% was computed when the phase-angle difference in forces was between approximately 90° and 120°. The results ultimately demonstrated that the vibration and foundation stiffness criteria could be met, which would have been more difficult without the use of a 3-D numerical modeling code

Transmission Map and Atmospheric Light Guided Iterative Updater Network for Single Image Dehazing

Hazy images obscure content visibility and hinder several subsequent computer vision tasks. For dehazing in a wide variety of hazy conditions, an end-to-end deep network jointly estimating the dehazed image along with suitable transmission map and atmospheric light for guidance could prove effective. To this end, we propose an Iterative Prior Updated Dehazing Network (IPUDN) based on a novel iterative update framework. We present a novel convolutional architecture to estimate channel-wise atmospheric light, which along with an estimated transmission map are used as priors for the dehazing network. Use of channel-wise atmospheric light allows our network to handle color casts in hazy images. In our IPUDN, the transmission map and atmospheric light estimates are updated iteratively using corresponding novel updater networks. The iterative mechanism is leveraged to gradually modify the estimates toward those appropriately representing the hazy condition. These updates occur jointly with the iterative estimation of the dehazed image using a convolutional neural network with LSTM driven recurrence, which introduces inter-iteration dependencies. Our approach is qualitatively and quantitatively found effective for synthetic and real-world hazy images depicting varied hazy conditions, and it outperforms the state-of-the-art. Thorough analyses of IPUDN through additional experiments and detailed ablation studies are also presented.Comment: First two authors contributed equally. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Project Website: https://aupendu.github.io/iterative-dehaz

Histopathological Image Analysis with Style-Augmented Feature Domain Mixing for Improved Generalization

Histopathological images are essential for medical diagnosis and treatment planning, but interpreting them accurately using machine learning can be challenging due to variations in tissue preparation, staining and imaging protocols. Domain generalization aims to address such limitations by enabling the learning models to generalize to new datasets or populations. Style transfer-based data augmentation is an emerging technique that can be used to improve the generalizability of machine learning models for histopathological images. However, existing style transfer-based methods can be computationally expensive, and they rely on artistic styles, which can negatively impact model accuracy. In this study, we propose a feature domain style mixing technique that uses adaptive instance normalization to generate style-augmented versions of images. We compare our proposed method with existing style transfer-based data augmentation methods and found that it performs similarly or better, despite requiring less computation and time. Our results demonstrate the potential of feature domain statistics mixing in the generalization of learning models for histopathological image analysis.Comment: Paper is published in MedAGI 2023 (MICCAI 2023 1st International Workshop on Foundation Models for General Medical AI) Code link: https://github.com/Vaibhav-Khamankar/FuseStyle Paper link: https://nbviewer.org/github/MedAGI/medagi.github.io/blob/main/src/assets/papers/P17.pd

A study of nutritional assessment of newly diagnosed tuberculosis patients in a tertiary care hospital of Tripura, India

Background: Tuberculosis kills more than any infection in India. TB is a serious public health problem in India. Tuberculosis causes immense morbidity. The mortality rate of this disease is also very high. Tuberculosis causes a great distress to the patients. To control this infection is a challenge to the health care facility of India. A lot of steps are being taken at various levels to end this disease. Still a huge number of patients are dying everyday from these deadly diseases. Out of so many recognised risk factors, malnutrition is considered to be as one of the most important among them. The immunity of a malnourished patient is suppressed. When the patient’s immunity is ineffective, the conversion of latent tuberculosis to diseases happens. Malnutrition invites tuberculosis and tuberculosis again causes morbidity, so there is a complex relation between this two. Malnutrition and tuberculosis are both problems of considerable magnitude in most of the underdeveloped regions of the world.Methods: In this cross sectional hospital based study involving 400 newly diagnosed Tuberculosis cases were taken. Their nutritional status was measured by BMI.Results: It was found that 66% of the study population is having malnutrition (BMI <18.5kg/m2). Malnutrition was more in females (71%). Mean BMI is 17.9Kg/m2. Mean height of the population is 1.53 meters.Conclusions: Nutritional supplementation may represent a novel approach for fast recovery in tuberculosis patients. In addition, raising nutritional status of population may prove to be an effective measure to control tuberculosis in underdeveloped areas of world. This study has demonstrated that half of newly diagnosed adult TB patients were malnourished at the time of starting treatment, with more than a quarter having moderate to severe malnutrition