Giant peripheral osteoma of the mandible

Osseous expansion of any body part is an unwelcome guest and deep are its impacts when it is located on the face. The bigger the lesion, the more bitter is the psychosocial trauma to the affected individual. This article describes the case of a 50 year old female who presented with painless swelling of the right submandibular region manifesting as a dreadful cosmetic disfigurement. The mass had been progressing slowly for the last 15 years. Imaging showed a giant peripheral osteoma of 10.8 cm involving buccal and lingual surface of the body, ramus, angle and inferior border of the right side of mandible. To the best of our knowledge, a giant peripheral osteoma of mandible having size more than 10 cm has never been reported earlier.KEYWORDS: Giant peripheral osteoma; Swelling of mandible; CT scan, Panoramic viewInternet Journal of Medical Update 2012 January;7(1):66-6

A novel approach for multispectral satellite image classification based on the bat algorithm

Amongst the multiple advantages and applications of remote sensing, one of the most important use is to solve the problem of crop classification, i.e., differentiating between various crop types. Satellite images are a reliable source for investigating the temporal changes in crop cultivated areas. In this work, we propose a novel Bat Algorithm (BA) based clustering approach for solving crop type classification problems using a multi-spectral satellite image. The proposed partitional clustering algorithm is used to extract information in the form of optimal cluster centers from training samples. The extracted cluster centers are then validated on test samples. A real-time multi-spectral satellite image and one benchmark dataset from the UCI repository are used to demonstrate robustness of the proposed algorithm. The performance of the Bat Algorithm is compared with the traditional K-means and two other nature-inspired metaheuristic techniques, namely, Genetic Algorithm and Particle Swarm Optimization. From the results obtained, we can conclude that BA can be successfully applied to solve crop type classification problems

DRBM-ClustNet: A Deep Restricted Boltzmann-Kohonen Architecture for Data Clustering

A Bayesian Deep Restricted Boltzmann-Kohonen architecture for data clustering termed as DRBM-ClustNet is proposed. This core-clustering engine consists of a Deep Restricted Boltzmann Machine (DRBM) for processing unlabeled data by creating new features that are uncorrelated and have large variance with each other. Next, the number of clusters are predicted using the Bayesian Information Criterion (BIC), followed by a Kohonen Network-based clustering layer. The processing of unlabeled data is done in three stages for efficient clustering of the non-linearly separable datasets. In the first stage, DRBM performs non-linear feature extraction by capturing the highly complex data representation by projecting the feature vectors of $d$ dimensions into $n$ dimensions. Most clustering algorithms require the number of clusters to be decided a priori, hence here to automate the number of clusters in the second stage we use BIC. In the third stage, the number of clusters derived from BIC forms the input for the Kohonen network, which performs clustering of the feature-extracted data obtained from the DRBM. This method overcomes the general disadvantages of clustering algorithms like the prior specification of the number of clusters, convergence to local optima and poor clustering accuracy on non-linear datasets. In this research we use two synthetic datasets, fifteen benchmark datasets from the UCI Machine Learning repository, and four image datasets to analyze the DRBM-ClustNet. The proposed framework is evaluated based on clustering accuracy and ranked against other state-of-the-art clustering methods. The obtained results demonstrate that the DRBM-ClustNet outperforms state-of-the-art clustering algorithms.Comment: 14 pages, 7 figure

Bursting Drops

For decades, researchers worldwide have investigated phenomena related to natural, artificial oil leakages such as oil drop formation within water bodies, their rise, and oil slick evolution after they breach the water-air interface. Despite this, the event leading to slick formation -the bursting of oil drops at the liquid-air interface has remained unnoticed thus far. In this work, we investigate this and report a counterintuitive jetting reversal that releases a daughter oil droplet inside the bulk as opposed to the upwards shooting jets observed in bursting air bubbles. We show that the daughter droplet size thus produced can be correlated to the bulk liquid properties and that its formation can be suppressed by increasing the bulk viscosity, by an overlaying layer of oil or by the addition of microparticles. We further demonstrate the significance of our results by synthesizing colloidal pickered droplets and show applications of bursting compound drops in double emulsions and studies on raindrop impact on a slick. These results could be immensely transformative for diverse areas, including climatology, oceanic, atmospheric sciences, colloidal synthesis and drug delivery

Condition Monitoring and Predictive Maintenance of Process Equipments

Industry 4.0 the proclaimed fourth industrial revolution is unfolding at the moment. It is characterized by interconnectedness and vast amounts of available information. Industrial production has evolved enormously over the last centuries due to modern instruments. Hence issue of the instrument failure is very paramount in any industry. Even if one machine fails it halts the whole production. Overall, it may cost us with more man-hours, project delay, process latency and all this sums up as a huge loss. The life of the instruments should be taken care by continuously monitoring its health. Any faulty or unnatural disturbance in usage of the instrument may lead to its failure. Every instrument needs proper maintenance, even with the slight negligence towards the anomaly it may lead to instrument failure. In, predictive maintenance historic data is utilized and analyzed with the help of advance analytics and modelling techniques using Machine learning, moreover we can predict failures and can schedule the maintenance beforehand and predict failure in advance. With the help of relevant sensor dataset, we can estimate the remaining runtime of the instruments. This maintenance approach helps to lower the costs which are incurred due to system shut downs. It also ease the scheduling and maintenance activities.In this work, three different industrial case studies are considered like shell and tube type heat exchanger, plate type heat exchanger, and semiconductor manufacturing process.Here the predictive maintenance is carried out for heat exchanger by utilizing the concept of multi linear regression and time series analysis. For the semiconductor manufacturing dataset, support vector machine algorithm is implemented to find out the good and bad quality of semiconductor production slots

Applications of Biomimicry in Construction and Architecture: A Bibliometric Analysis

Biomimicry can be considered to be a way of connecting the environment created by man to the natural world. Biomimicry is a science that, as a model, a measure and a tutor, looks to nature. Via site work, construction and everyday operations, biomimicry can be used to enhance the way the built environment is constructed. The main objective of this paper is to perform a bibliometric study of biomimicry-related literature in order to discover the growth of biomimicry as an architectural method in recent years. The time frame considered for this survey is between 1990 and 2020. The findings, however, indicate that the first paper was not written until 2007. In this paper, bibliometric analysis focuses primarily on results from the Scopus database. For data visualization purposes, external software like iMapBuilder and VOSviewer are used. The research is intended to show the need for biomimicry in the modern world. The result of the study sheds light on the lack of biomimicry research and the need for more research on the subject. A new research approach for comprehensive research in biomimicry paves the way for the results of this analysis

Evolution of Codon Usage Bias in Henipaviruses Is Governed by Natural Selection and Is Host-Specific

Hendra virus (HeV) and Nipah virus (NiV) are among a group of emerging bat-borne paramyxoviruses that have crossed their species-barrier several times by infecting several hosts with a high fatality rate in human beings. Despite the fatal nature of their infection, a comprehensive study to explore their evolution and adaptation in different hosts is lacking. A study of codon usage patterns in henipaviruses may provide some fruitful insight into their evolutionary processes of synonymous codon usage and host-adapted evolution. Here, we performed a systematic evolutionary and codon usage bias analysis of henipaviruses. We found a low codon usage bias in the coding sequences of henipaviruses and that natural selection, mutation pressure, and nucleotide compositions shapes the codon usage patterns of henipaviruses, with natural selection being more important than the others. Also, henipaviruses showed the highest level of adaptation to bats of the genus Pteropus in the codon adaptation index (CAI), relative to the codon de-optimization index (RCDI), and similarity index (SiD) analyses. Furthermore, a comparison to recently identified henipa-like viruses indicated a high tRNA adaptation index of henipaviruses for human beings, mainly due to F, G and L proteins. Consequently, the study concedes the substantial emergence of henipaviruses in human beings, particularly when paired with frequent exposure to direct/indirect bat excretions