The stress and free radical towards disease and aging.

All living things will grow old, and inevitably all will die one day. This is a normal process to grow old, to have disease and die. Death is no exception. However, the rate of disease occurrence varies with different people. Moreover, the psychological distress influences the onset of age related disease and biological aging. Actually, aging is a multi-factorial process, and many contributing mechanisms have not clearly been identified yet. However, there are so many theories have been explained the underlying biological basis of aging process (Curtler, 1994). The various interactive factors which contribute to the aging process have been shown in figur

Myanmar news summarization using different word representations

There is enormous amount information available in different forms of sources and genres. In order to extract useful information from a massive amount of data, automatic mechanism is required. The text summarization systems assist with content reduction keeping the important information and filtering the non-important parts of the text. Good document representation is really important in text summarization to get relevant information. Bag-of-words cannot give word similarity on syntactic and semantic relationship. Word embedding can give good document representation to capture and encode the semantic relation between words. Therefore, centroid based on word embedding representation is employed in this paper. Myanmar news summarization based on different word embedding is proposed. In this paper, Myanmar local and international news are summarized using centroid-based word embedding summarizer using the effectiveness of word representation approach, word embedding. Experiments were done on Myanmar local and international news dataset using different word embedding models and the results are compared with performance of bag-of-words summarization. Centroid summarization using word embedding performs comprehensively better than centroid summarization using bag-of-words

Developing Word-aligned Myanmar-English Parallel Corpus based on the IBM Models

Word alignment in bilingual corpora has been an active research topic in the Machine Translation research groups. Corpus is the body of text collections, which are useful for Language Processing (NLP). Parallel text alignment is the identification of the corresponding sentences in the parallel text. Large collections of parallel level are prerequisite for many areas of linguistic research. Parallel corpus helps in making statistical bilingual dictionary, in supporting statistical machine translation and in supporting as training data for word sense disambiguation and translation disambiguation. Nowadays, the world is a global network and everybody will be learned more than one language. So, multilingual corpora are more processing. Thus, the main purpose of this system is to construct word-aligned parallel corpus to be able in Myanmar-English machine translation. One useful concept is to identify correspondences between words in one language and in other language. The proposed approach is based on the first three IBM models and EM algorithm. It also shows that the approach can also be improved by using a list of cognates and morphological analysis

Improving accuracy of Part-of-Speech (POS) tagging using hidden markov model and morphological analysis for Myanmar Language

In Natural Language Processing (NLP), Word segmentation and Part-of-Speech (POS) tagging are fundamental tasks. The POS information is also necessary in NLP’s preprocessing work applications such as machine translation (MT), information retrieval (IR), etc. Currently, there are many research efforts in word segmentation and POS tagging developed separately with different methods to get high performance and accuracy. For Myanmar Language, there are also separate word segmentors and POS taggers based on statistical approaches such as Neural Network (NN) and Hidden Markov Models (HMMs). But, as the Myanmar language's complex morphological structure, the OOV problem still exists. To keep away from error and improve segmentation by utilizing POS data, segmentation and labeling should be possible at the same time.The main goal of developing POS tagger for any Language is to improve accuracy of tagging and remove ambiguity in sentences due to language structure. This paper focuses on developing word segmentation and Part-of- Speech (POS) Tagger for Myanmar Language. This paper presented the comparison of separate word segmentation and POS tagging with joint word segmentation and POS tagging

Source side pre-ordering using recurrent neural networks for English-Myanmar machine translation

Word reordering has remained one of the challenging problems for machine translation when translating between language pairs with different word orders e.g. English and Myanmar. Without reordering between these languages, a source sentence may be translated directly with similar word order and translation can not be meaningful. Myanmar is a subject-objectverb (SOV) language and an effective reordering is essential for translation. In this paper, we applied a pre-ordering approach using recurrent neural networks to pre-order words of the source Myanmar sentence into target English’s word order. This neural pre-ordering model is automatically derived from parallel word-aligned data with syntactic and lexical features based on dependency parse trees of the source sentences. This can generate arbitrary permutations that may be non-local on the sentence and can be combined into English-Myanmar machine translation. We exploited the model to reorder English sentences into Myanmar-like word order as a preprocessing stage for machine translation, obtaining improvements quality comparable to baseline rule-based pre-ordering approach on asian language treebank (ALT) corpus

Myanmar named entity corpus and its use in syllable-based neural named entity recognition

Myanmar language is a low-resource language and this is one of the main reasons why Myanmar Natural Language Processing lagged behind compared to other languages. Currently, there is no publicly available named entity corpus for Myanmar language. As part of this work, a very first manually annotated Named Entity tagged corpus for Myanmar language was developed and proposed to support the evaluation of named entity extraction. At present, our named entity corpus contains approximately 170,000 name entities and 60,000 sentences. This work also contributes the first evaluation of various deep neural network architectures on Myanmar Named Entity Recognition. Experimental results of the 10-fold cross validation revealed that syllable-based neural sequence models without additional feature engineering can give better results compared to baseline CRF model. This work also aims to discover the effectiveness of neural network approaches to textual processing for Myanmar language as well as to promote future research works on this understudied language

Osmotic Dehydration of Toddy Fruit Cubes in Sugar Solution Using Response Surface Methodology

The response surface methodology (RSM) was applied to optimize the effects of immersion time (60, 90 and 120 min), temperature (35, 45 and 55°C) and concentration of sucrose solution (30, 40 and 50°Brix) in osmotic dehydration of toddy fruit tubes (1cm3). Box-Behnken Design was used with water loss (WL, %), solid gain (SG, %), and weight reduction (WR, %) as responses. The models obtained for all the responses were significant (P≤0.05) without a significant lack of fit. The optimum conditions were temperature (45°C), immersion time (120min), concentration of sucrose solution (40°Brix) in order to obtain WL of (33.867g/100g initial sample), SG of (4.478g/100g initial sample) and WR of 29.39 g/100g initial sample, respectively

Study on the Investigation of Selected Toxic Metals and Essential Metals on Different Brands (Local and Imported) Canned Fish

Metal pollution of water ways directly affects human health and can impact the food chain. Toxic trace metals in canned foods may occur as a result of polluted water or by migration from the packing material. The purpose of in this study was to analyze the concentration of selected toxic elements including Pb, Cu, Cd and dietary metals Zn and Mn in 10 each can from five different brands (local and imported) of canned tuna fish were quantitatively determined by acid digesting method and atomic absorption spectroscopic method. All samples were collected from different city marts in Yangon. The concentration of metals (toxic and essential) in five different brands were found to be observed in the range of 0.0195 to 0.0209 mg/ l for Mn, 0.300 to 0.304 mg/l for Zn, 0.053 to 0.039 mg/l for Cd of local brands. In imported canned tuna brands, the concentration of Mn and Zn, were observed in the range of 0.0171 to 0.0218 mg/l, and 0.261 to 0.343 mg/l. The concentration of some toxic elements Cd, Pb and Cu in different imported brands were observed in the range of 0.044 to 0.051 mg/l for Cd. The concentration of Pb and Cu were not detected in all brands (local and imported) of tuna canned. The average concentrations of all toxic and essential metals were much lower than the reference limit (WHO/FAO). Based on the obtained results it can be concluded that the all canned samples (local and imported) are free from heavy metal contaminatio