Ecological Restoration of Degraded Habitats of Jajang Iron and Manganese Ore Mines, Keonjhar, Odisha, India

Mining activities in Jajang iron and manganese ore mines located in Keonjhar district of Odisha, India starting from mineral explorations to production and transport are causing environmental damage in many ways, which includes deforestation, loss of topsoil, accelerated soil erosion, migration of wildlife and avifauna, and addition of air pollutants and dust to the atmosphere. In connection to this, the current study was an attempt to regain the original ecological status of the degraded areas of Jajang iron and manganese ore mines caused due to mining by Rungta Mines Limited. To achieve this indigenous plant species for restoration were selected from mining forests and plantations. Species selection from mining forests was made through systematic phytosociological analysis that involved measurement of Importance Value Index (IVI), regeneration values of tree species and their economic uses. On the other hand, species selection from plantations was made based on their growth, productivity, economic uses and adaptation to terrain and soil types. Shrubs and grasses were selected based on their relative index and abundance, respectively. The top 15 tree and 16 grass species as well as all six shrub species were selected from mining forests and plantations were considered for restoration. The findings of the study may also aids in the faster restoration of degraded habitats with initial human facilitation as the soils of degraded areas were similar to that of the mining forest. To speed up the recovery process after-care and monitoring have also been suggested or advised

An approach for mistranslation removal from popular dataset for Indic MT Task

The conversion of content from one language to another utilizing a computer system is known as Machine Translation (MT). Various techniques have come up to ensure effective translations that retain the contextual and lexical interpretation of the source language. End-to-end Neural Machine Translation (NMT) is a popular technique and it is now widely used in real-world MT systems. Massive amounts of parallel datasets (sentences in one language alongside translations in another) are required for MT systems. These datasets are crucial for an MT system to learn linguistic structures and patterns of both languages during the training phase. One such dataset is Samanantar, the largest publicly accessible parallel dataset for Indian languages (ILs). Since the corpus has been gathered from various sources, it contains many incorrect translations. Hence, the MT systems built using this dataset cannot perform to their usual potential. In this paper, we propose an algorithm to remove mistranslations from the training corpus and evaluate its performance and efficiency. Two Indic languages (ILs), namely, Hindi (HIN) and Odia (ODI) are chosen for the experiment. A baseline NMT system is built for these two ILs, and the effect of different dataset sizes is also investigated. The quality of the translations in the experiment is evaluated using standard metrics such as BLEU, METEOR, and RIBES. From the results, it is observed that removing the incorrect translation from the dataset makes the translation quality better. It is also noticed that, despite the fact that the ILs-English and English-ILs systems are trained using the same corpus, ILs-English works more effectively across all the evaluation metrics.Comment: 18 page

Multilingual Neural Machine Translation System for Indic to Indic Languages

This paper gives an Indic-to-Indic (IL-IL) MNMT baseline model for 11 ILs implemented on the Samanantar corpus and analyzed on the Flores-200 corpus. All the models are evaluated using the BLEU score. In addition, the languages are classified under three groups namely East Indo- Aryan (EI), Dravidian (DR), and West Indo-Aryan (WI). The effect of language relatedness on MNMT model efficiency is studied. Owing to the presence of large corpora from English (EN) to ILs, MNMT IL-IL models using EN as a pivot are also built and examined. To achieve this, English- Indic (EN-IL) models are also developed, with and without the usage of related languages. Results reveal that using related languages is beneficial for the WI group only, while it is detrimental for the EI group and shows an inconclusive effect on the DR group, but it is useful for EN-IL models. Thus, related language groups are used to develop pivot MNMT models. Furthermore, the IL corpora are transliterated from the corresponding scripts to a modified ITRANS script, and the best MNMT models from the previous approaches are built on the transliterated corpus. It is observed that the usage of pivot models greatly improves MNMT baselines with AS-TA achieving the minimum BLEU score and PA-HI achieving the maximum score. Among languages, AS, ML, and TA achieve the lowest BLEU score, whereas HI, PA, and GU perform the best. Transliteration also helps the models with few exceptions. The best increment of scores is observed in ML, TA, and BN and the worst average increment is observed in KN, HI, and PA, across all languages. The best model obtained is the PA-HI language pair trained on PAWI transliterated corpus which gives 24.29 BLEU.Comment: 38 pages, 2 figure

Improving Multilingual Neural Machine Translation System for Indic Languages

Machine Translation System (MTS) serves as an effective tool for communication by translating text or speech from one language to another language. The need of an efficient translation system becomes obvious in a large multilingual environment like India, where English and a set of Indian Languages (ILs) are officially used. In contrast with English, ILs are still entreated as low-resource languages due to unavailability of corpora. In order to address such asymmetric nature, multilingual neural machine translation (MNMT) system evolves as an ideal approach in this direction. In this paper, we propose a MNMT system to address the issues related to low-resource language translation. Our model comprises of two MNMT systems i.e. for English-Indic (one-to-many) and the other for Indic-English (many-to-one) with a shared encoder-decoder containing 15 language pairs (30 translation directions). Since most of IL pairs have scanty amount of parallel corpora, not sufficient for training any machine translation model. We explore various augmentation strategies to improve overall translation quality through the proposed model. A state-of-the-art transformer architecture is used to realize the proposed model. Trials over a good amount of data reveal its superiority over the conventional models. In addition, the paper addresses the use of language relationships (in terms of dialect, script, etc.), particularly about the role of high-resource languages of the same family in boosting the performance of low-resource languages. Moreover, the experimental results also show the advantage of backtranslation and domain adaptation for ILs to enhance the translation quality of both source and target languages. Using all these key approaches, our proposed model emerges to be more efficient than the baseline model in terms of evaluation metrics i.e BLEU (BiLingual Evaluation Understudy) score for a set of ILs.Comment: 26 pages, 10 figures, 7 table

An Observational Study on Neonatal Seizures in a Tertiary Care Hospital

Background: Seizures are the most frequent clinical manifestation of central nervous system dysfunction in the newborn with the incidence varying from 1-5%. Neonatal seizures often signal an underlying ominous neurological condition, most commonly hypoxia-ischemia, and others include stroke, intraventricular hemorrhage or intraparenchymal hemorrhage, meningitis, sepsis, and metabolic disorders. Neonatal seizures can permanently disrupt neuronal development, induce synaptic reorganization, alter plasticity and "prime" the brain to increased damage from seizures later in life. The objective of this study was to observe neonatal seizures in a Tertiary Care Hospital. Methods: This study was a hospital-based, prospective, observational study conducted in the sick new born care unit of department of pediatrics in a tertiary care hospital from March 2017 to February 2018. Out of 2654 admitted neonates, 234 notates having symptom of seizures were included in the study after informed consent from the mother of the neonate. The data like history, clinical examination and investigation findings was recorded in the pre-designed, pre-tested, semi structured questionnaire. Template was generated in MS excel sheet and analysis was done on SPSS software. Results : The incidence of neonatal seizures was higher in male neonates. Subtle types of seizures were the commonest type of seizures. out of 234 neonates, 68 (29.06%) were preterm while 166 (70.94%) were term neonates. Out of 68 term neonates, 26 (38.23%) neonates had subtle seizures, 16 (23.52%) neonates had focal clonic seizures. Out of 166 term neonates, 56 (33.73%) neonates had subtle seizures, 42 (25.30%) neonates had focal clonic seizures. Almost 68 (29.05%) developed seizures within 24 hours, 84 (35.91%) neonates had seizures between 25-48 hours, 54 (23.08%) neonates developed seizures between 2-7 days and 28 (11.96%) neonates developed seizures after 7 days. Common causes of neonatal deaths in our center were severe birth asphyxia, intra-ventricular hemorrhage (IVH), septicemia and meningitis. Conclusions: Neonatal seizures are common and may be the first manifestation of neurological dysfunction after a variety of insults. Most of the causes of neonatal seizures are preventable by good perinatal care and early interventions while metabolic seizures need a sharp vigilance and early suspicion

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Not AvailableComparative evaluation of different farming system models suitable for small and marginal farmers of NagalandNot Availabl

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Not AvailableComparative evaluation of different farming system models suitable for small and marginal farmers of NagalandNot Availabl

Preventing soil degradation in shifting cultivation using integrated farming system models

Not AvailableIntegrated farming system (IFS) based shifting cultivation reduces soil erosion and outperform in terms of productivity and income generation in North-Eastern Hills of India. To address this hypothesis, an experiment was conducted at Jharnapani, Nagaland, India with five treatments, viz., natural vegetation (NV), animal-based IFS (IFA), horticulture-based IFS (IFH), traditional shifting cultivation (TS) and traditional shifting cultivation with minor interventions (TSM). The soil moisture (34%), available N (23%), soil organic carbon (35%), bacterial (56%) and fungal (54%) counts decreased after burning of natural vegetation. Burning increased soil pH (10%), EC (131%), available P (62%), and available K (53%) for a short period. Improved integrated farming system models such as IFA and IFH were found to reduce soil erosion (34–48%) and loss of SOM (26–51%), N (33–45%), P (19–54%) and K (27–51%) compared to the traditional system (TS). Moreover, the improved models like IFA and IFH had a higher net return (0.07–52% higher) compared to TS. Considering all these facts, IFA/IFH can be recommended for the North Eastern Hill region based on suitability of altitude, socio-economic status of the farmers, farmers’ preference and demand in the local market.Not Availabl

Investigation on chemical protease, nuclease and catecholase activity of two copper complexes with flexidentate Schiff base ligands

Two new Cu(II) complexes [Cu(HL)(MeOH)(Py)](ClO4)2 (1), [Cu(HL)(DMF])(NO3)2 (2) have been synthesized from Schiff base ligand [HL = 2-(phenyl((2-(piperazin-1-yl)ethyl)imino)methyl)phenol ] with flexible piperazinyl moiety. Structural analysis reveals that 1 and 2 are monomeric Cu(II) complex consisting of penta and tetra coordinated Cu(II) centers, respectively. Screening tests were conducted to quantify the binding ability of complexes (1 and 2) towards BSA and DNA as well as the protease and nuclease activity of these complexes using gel electrophoresis technique. Furthermore enzyme kinetic studies were also performed for those two complexes towards effectiveness in mimicking catecholase like activities. Overall all the experimental results reveal the potential activity of these copper complexes towards protease, nuclease and catecholase activity. Apart from these, MTT assay was also utilized to scrutinize the anti-proliferative activity which was further investigated using dual staining confocal microscopic images