Monthly Variation in Plant Biomass and Net Primary Productivity of a Mixed Deciduous Forest at Foothills of Kumaun Himalaya

Variation in plant biomass and net primary productivity of a forest grazing land vegetation was analyzed in Tarai of Kumaun Himalaya near Kashipur. The vegetation was studied at monthly intervals from April 2007 to April 2008. Aboveground green biomass, dead biomass, litter biomass and belowground biomass were recorded as 216.93±42.42 g•m-2, 15.30±8.65 g•m-2, 5.52±2.31 g•m-2 and 132.91±41.70 g•m-2 respectively. Average total plant biomass was 365.15±70.23 g•m-2. The aboveground, belowground and litter production were 372.33 g•m-2, 339.70 g•m-2 and 13.91 g•m-2 respectively. Total net primary production was 712.03±23.07 g•m-2. The turnover rates were 1.17, 1.20 and 1.47 for aboveground, litter and belowground parts respectively. Based on system transfer function, the net surplus of organic material as the rate of disappearance was moderately half than the rate of dry matter accumulation

Health System Using Social Network to detect the Dengue infection in the specific region

Background: There is law in some countries that each and every health-related event should be recorded or documented digitally and centrally. Unfortunately, this practice is not implemented due to various reasons, that is why the real ground realities differ from what agencies says. Social Network is one of the fastest and effective ways to propagate information without any discrimination and condition. Aims & Objective: By using this beauty of the social network, we proposed a Health System using twitter to detect dengue infected people in the specific region so the concerned agencies take appropriate action proactively and can avoid the epidemic like condition. Material & Methods: Using Naïve Bayes classifier we classify incoming tweets into two categories weather it is promotional tweet or it tells about a dengue infected patient. The method and the proposed health system presented in this article can be applied to another similar task, like to detect communicable disease patients in the specified location. Results: The proposed system is capable enough to give the feel of dengue in the particular region. The results show that the developed system’s efficiency is 57% when testing performs on three set of collected tweets. Conclusion: Twitter can be effectively to get the health status of the society and warned the concerned agencies before the emergency like situation

Forecasting the Missing Links in Heterogeneous Social Networks

Social network analysis has gained attention from several researchers in the past time because of its wide application in capturing social interactions. One of the aims of social network analysis is to recover missing links between the users which may exist in the future but have not yet appeared due to incomplete data. The prediction of hidden or missing links in criminal networks is also a significant problem. The collection of criminal data from these networks appears to be incomplete and inconsistent which is reflected in the structure in the form of missing nodes and links. Many machine learning algorithms are applied for this detection using supervised techniques. But, supervised machine learning algorithms require large datasets for training the link prediction model for achieving optimum results. In this research, we have used a Facebook dataset to solve the problem of link prediction in a network. The two machine learning classifiers applied are LogisticRegression and K-Nearest Neighbour where KNN has higher accuracy than LR. In this article, we have proposed an algorithm Graph Sample Aggregator with Low Reciprocity, (GraphSALR), for the generation of node embeddings in larger graphs which use node feature information

PeopleNet: A Novel People Counting Framework for Head-Mounted Moving Camera Videos

Traditional crowd counting (optical flow or feature matching) techniques have been upgraded to deep learning (DL) models due to their lack of automatic feature extraction and low-precision outcomes. Most of these models were tested on surveillance scene crowd datasets captured by stationary shooting equipment. It is very challenging to perform people counting from the videos shot with a head-mounted moving camera; this is mainly due to mixing the temporal information of the moving crowd with the induced camera motion. This study proposed a transfer learning-based PeopleNet model to tackle this significant problem. For this, we have made some significant changes to the standard VGG16 model, by disabling top convolutional blocks and replacing its standard fully connected layers with some new fully connected and dense layers. The strong transfer learning capability of the VGG16 network yields in-depth insights of the PeopleNet into the good quality of density maps resulting in highly accurate crowd estimation. The performance of the proposed model has been tested over a self-generated image database prepared from moving camera video clips, as there is no public and benchmark dataset for this work. The proposed framework has given promising results on various crowd categories such as dense, sparse, average, etc. To ensure versatility, we have done self and cross-evaluation on various crowd counting models and datasets, which proves the importance of the PeopleNet model in adverse defense of society

DiRiboPred: A Web Tool for Classification and Prediction of Ribonucleases

Ribonuclease [commonly abbreviated RNase] is a type of nuclease that catalyzes the degradation of RNA into smaller components and can be divided into endoribonucleases and exoribonucleases. All organisms studied contain many RNases of many different classes, showing that RNA degradation is a very ancient and important process. They are shown to have an important role in cancer, tumor and many neuro degenarative disorders for controlling which, in-silico drug designing can be a valuable tool. In the past machine learning has been used to classify other proteins like GPCRs but no attempt has been made for classification of Ribonucleases. Realizing their importance here an attempt has been made to develop an SVM model to predict, classify and correlate all the major subclasses of ribonucleases with their dipeptide composition. The method was trained and tested on 1857 proteins of ribonucleases. The method discriminated Ribonucleases from other enzymes with Matthew\'s correlation coefficient of 1.00 and 100% accuracy. In classifying different subclasses of Ribonucleases with dipeptide composition, an overall accuracy of 94.534% was achieved. The performance of the method was evaluated using 5-fold cross-validation. A web server DiRiboPred has been developed for predicting Ribonucleases from its amino acid sequence http://www.bif.manit.org/RiboPred2

ICAR: endoscopic skull‐base surgery

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<span style="font-size:11.0pt;mso-bidi-font-size: 10.0pt;font-family:"Times New Roman";mso-fareast-font-family:"Times New Roman"; mso-ansi-language:EN-GB;mso-fareast-language:EN-US;mso-bidi-language:AR-SA" lang="EN-GB">Ethnobotanical study of some common plants used among the tribal communities of Kashipur, Uttarakhand</span>

262-266<span style="font-size:11.0pt;mso-bidi-font-size: 9.0pt;font-family:" times="" new="" roman";mso-fareast-font-family:"times="" roman";="" letter-spacing:-.1pt;mso-ansi-language:en-gb;mso-fareast-language:en-us;="" mso-bidi-language:ar-sa"="" lang="EN-GB">The present study highlights traditionally used medicinal plants by the tribal communities in Kashipur (Uttrakhand). A total of 22 common plants belonging to 16 families used to cure human diseases and in which 1 plant species was used to cure diseases of cattle of the local communities. Based on life form 07 therophytes, 14 phanerophytes and 01 chamaephyte were recorded. Local communities, especially, older people including women frequently use these locally available medicinal plants for their health and believe that these are easily available, less expensive and have no side effects.</span