Processing and Characterization of Activated Carbon from Toddy Fruit Husk

The focus of this research work was the processing and characterization of activated carbon from toddy palm fruit husk. To identify the purity of raw material, the properties of toddy palm fruit husk in terms of moisture, ash, volatile matter, fixed carbon content and bulk density were determined. Activated carbon was prepared by chemical activation of toddy palm fruit husk and then by carbonization. The effect of carbonization temperature and time on the appearance and properties of processed activated carbons were investigated. The physico-chemical properties of processed activated carbon such as moisture, ash, volatile matter and fixed carbon content were determined. The highest fixed carbon content was obtained with 25 % w/w zinc chloride as chemical activating agent at carbonization temperature 350°C for carbonization time 45 min. For the assessment of the quality of the product, the properties such as surface area, iodine sorption capacity and methylene blue number were also investigated. Furthermore, the phase and microstructure of the prepared activated carbons were determined by X-Ray Diffraction Spectroscopy (XRD) and Scanning Electron Microscopy (SEM). The high iodine sorption capacity and fairly high methylene blue number indicated that the processed activated carbon had large surface area and well developed mesoporosity. Therefore toddy palm fruit was the potential to be a promising precursor for the processing of activated carbon

Association of Screen Time and Body Mass Index

The global prevalence of obesity is increasing in adults as well as in children. Prevention of overweight and obesity in children is necessary because childhood obesity is associated with a wide range of serious complications such as type 2 diabetes, cardiovascular diseases and psychological problems. Several lifestyle factors have been implicated as determinants of childhood obesity. One potential contributor to childhood obesity is time spent with screen media. In recent decades, screen time occupies a prominent place in children’s environment. There are several studies in association of screen time and childhood obesity in developed countries

Domain-specific Sentiment Dictionary Construction for Sentiment Classification

Sentiment dictionaries are commonly used to solve the problem of sentiment classification for customer reviews. The number of sentiment words in the generalized dictionaries such as SentiWordNet is limited and lack of many sentiment words especially domain-specific sentiment words. Different domains have different sentiment words and the sentiment of a word depends on the domain in which it is used. In this paper, an approach based on Point-wise Mutual Information (PMI) is proposed to construct a domain-specific sentiment dictionary effectively and automatically. The proposed system is evaluated on three diverse datasets from different domains by using 10-fold cross validation. Accordingly to the experimental results, the goodness of the extracted dictionary is relatively high and significantly improves the performance of sentiment classification. The experimental results show that the extracted domain-specific dictionary outperforms the generalized dictionary, SentiWordNet. The proposed method learns the domain-specific sentiment words efficiently and it is domain adaptable

Back pain in an elderly patient - A case report

ABSTRACT Low back pain (LBP) secondary to the degenerative spinal disorder is a very common medical condition that presents in elderly people and rarely indicates a serious illness. We would like to report a case of 83 years old gentleman with a history of chronic degenerative back pain with the change in the nature of back pain, which triggered us to arrange further investigations and diagnosed Psoas abscess(PA) secondary to septic vertebral arthritis. It was treated with CT-guided drainage and sensitive antibiotics. This case report highlights the atypical presentation of diseases in elderly patients, and the common pitfalls of missing serious pathologies, which increases morbidity and mortality. Keywords: low back pain, septic arthritis, Psoas absces

UCSY-SC1: A Myanmar speech corpus for automatic speech recognition

This paper introduces a speech corpus which is developed for Myanmar Automatic Speech Recognition (ASR) research. Automatic Speech Recognition (ASR) research has been conducted by the researchers around the world to improve their language technologies. Speech corpora are important in developing the ASR and the creation of the corpora is necessary especially for low-resourced languages. Myanmar language can be regarded as a low-resourced language because of lack of pre-created resources for speech processing research. In this work, a speech corpus named UCSY-SC1 (University of Computer Studies Yangon - Speech Corpus1) is created for Myanmar ASR research. The corpus consists of two types of domain: news and daily conversations. The total size of the speech corpus is over 42 hrs. There are 25 hrs of web news and 17 hrs of conversational recorded data.The corpus was collected from 177 females and 84 males for the news data and 42 females and 4 males for conversational domain. This corpus was used as training data for developing Myanmar ASR. Three different types of acoustic models  such as Gaussian Mixture Model (GMM) - Hidden Markov Model (HMM), Deep Neural Network (DNN), and Convolutional Neural Network (CNN) models were built and compared their results. Experiments were conducted on different data  sizes and evaluation is done by two test sets: TestSet1, web news and TestSet2, recorded conversational data. It showed that the performance of Myanmar ASRs using this corpus gave satisfiable results on both test sets. The Myanmar ASR  using this corpus leading to word error rates of 15.61% on TestSet1 and 24.43% on TestSet2

Weight-based Word Sense Disambiguation Method for Myanmar-to-English Language Translation

In many natural language processing (NLP) techniques, machine translation is a popular and useful technique. Machine translation technique is a translation process from one to another language. This technique is thus very useful for people around the world. While translating the languages, ambiguity is a big challenge because many words have several meanings. Ambiguous words have damaging effects on the precision of machine translation. To solve this problem, word sense disambiguation (WSD) method is useful for automatically identifying the correct meaning of an ambiguous word. In order to have a better precision, weight-based WSD method is proposed by taking advantage of a Minkowski distance method. As the proposed method considers the weight values of each sense of training and input vectors while observing the ambiguous words, it is more effective than the simple translation system. Experimental results show that the weight-based WSD method gives a better precision approximately 51% when compared to the simple machine translation method