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A challenging task in Opinion Mining is to detect sarcasm. Sarcasm is a way of telling things in a positive way where the intended meaning is negative. Sarcastic texts lead to wrong classification of data due to the usage of positive words to convey negative emotions. It is important to detect sarcasm on social media data because there is no face-to-face conversation happening to understand the context. When this data is taken for sentiment analysis and decision-making process it leads to wrong inferences. The proposed work classifies the dataset into sarcastic and non-sarcastic using machine learning algorithms and the same has been implemented using R and Python for finding the difference in accuracy. The dataset has been collected from Twitter. Naive Bayes algorithm is used for the classification. The result obtained from sarcasm detection on social media data will be helpful in areas such as sentiment analysis, natural language processing, opinion mining, business-related decision making, and so on. The result of the proposed method classified the given data into sarcastic and non-sarcastic with an accuracy of 0.91 and 0.88 using R and Python respectively.

Introduction. Cheyne-Stokes breathing (CSB) is described by a cyclic variation in breathing with periods of central apneas or hypopneas alternating with periods of hyperpneas in a gradual waxing and waning manner. We describe this rare case of idiopathic central sleep apnea coming in a periodic fashion presenting as CSB pattern without specific causes. Results. A 72-year-old male (body mass index: 21.9 kg/m2) with the complaints of excessive daytime sleepiness and fatigue for about 2 years. On detailed sleep history patient denied snoring, cataplexy, hypnogogic hallucination, sleep paralysis and sleep attack. No neurological deficit was seen in clinical evaluation. ENT evaluation was normal. Cardiac evaluation showed artrial fibrillation with no cardiac failure. An overnight Polysomnography (PSG) test showed total sleep time of 443 minutes, sleep latency of 19.0 min, and sleep efficiency of 70.0%. Sleep architecture for stage 1 sleep was 31.7%, stage 2 sleep was 52.4%, slow wave sleep was 12.9%, REM sleep was 3.1%. Total arousal index was 21.8/hr. Mean SaO2 was 96.4%, the lowest SaO2 was 82.8%, and SaO2 below 90% was 0.1%. Snoring was not observed and periodic limb movement index was 8.8/hr. PSG also showed cyclic fluctuation in breathing with periods of central apneas; alternating with periods of hyperpneas in a gradual waxing and waning fashion; but cardiac evaluation and magnetic resonance imaging of the brain could not find any cause of this sleep-disordered breathing. Conclusion. The pathogenesis and etiology of CSA is still vague. We think that further studies and follow up will be required to determine whether idiopathic central sleep apnea presenting as CSB pattern is the initial indication of certain diseases. So we concluded that this case was an idiopathic central sleep apnea with periodicity; presenting as CSB pattern.

Nano-imprinting technology, as one of the most promising fabrication technologies, has been demonstrated to be a powerful tool for large-area replication up to wafer-level, with features down to nanometer scale. This study aims to develop capabilities in patterning nano structure using thermal nano-imprint lithography (NIL). 30nm Si molds are patterned by electron-beam lithography (EBL) using NEB22 A2 negative e-beam resist. The NEB22 A2 negative e-beam resist possess a variety of characteristics desirable for NIL, such as low viscosity, low bulk-volumetric shrinkage, high Young's modulus, high thermal stability, and excellent dry-etch resistance. The excellent oxygenetch resistance of the barrier material enables a final transfer pattern that is about three times higher than that of the original NIL mold. Based on these imprint on negative electron beam resist approach is used for pattern transfer into silicon substrates. The result is a high-resolution pattern with feature sizes in the range of nanometer to several microns.