Peack expiratory flow rate in South Indian Children

PEFR is a simple and reliable way of following patients with bronchial asthma and other obstructive airway diseases. Normal data is available for Caucasian and North Indian children but not for ethnic South Indian children. We, therefore, measured Peak Expiratory Flow Rate (PEFR) in 343 healthy South Indian children aged 4-15 years, using the Wright mini peak flow meter. A nomogram was constructed relating PEFR to height. Prediction equations for PEFR using height alone or height, age and weight were determined for both sexes. The prediction equation for boys based on height alone was PEFR = 4.08 height (cm) - 284.55 and for girls was PEFR = 3.92 height (cm) - 277.01

A study on counting patterns in preschool children (4-5 years old)

Counting is one of the first mathematical concepts that children learn. Children learn to count both formally and informally through interaction with others in their environment Most of the children learn the mathematical concept counting during the pre-school stage. There are research findings (Huges, 1986) which show that most of the preschool children face the problem of counting. The objectives of the study: 1. Examine the patterns of counting numbers applied by the preschool children. 2. Identify the differences of counting patterns of boys and girls in the sample. 3. Identify the pattern of counting errors made by the children in the sample. Target population for the study comprised preschool children. Representative sample was selected from the Western province. Stratified random sampling method was used for selecting the sample of preschools and simple random sampling method was used for selecting 10 children from each class. The research has established some major facts which need to be tested in a larger scale sample which would cover more than one province. On the other hand this sample researched only the areas in the Western province which records a higher stage of development in human and physical capital which other provinces of the country have achieved unevenly. And therefore the findings should not be taken for granted for the whole country. According to the findings we can sate that teaching instruction procedure has to be designed to eliminate the problems encountered by the preschool child when counting numbers. Further the teacher has to pay individual attention on children and he/she should provide practice session to children on the pronunciation of number words. Further the study points at the parental and teacher caring of the children in guiding children to acquire the basic mathematical and language skills b y using correct language with children preparing necessary environmental factors

A STUDY ON USE OF FACEBOOK BY PG STUDENTS OF SELECTED DEPARTMENTS IN SRI RAMAKRISNA COLLEGE OF ARTS AND SCIENCE, COIMBATORE

The study on use of face book by students is a paper that represents a evaluation of more than one hundred studies on the social networking website face book. The paper focuses on face book use, which is currently the most researched theme the main studies for each group are presented, with an emphasis on the most influential ones in the field. The focus of this fictional examination is on the commonalities and difference that start from the results. As a result, one could notice that face book is mainly used to keep in touch with other people, but not in a conventional way as uses tend to spy on other users profiles this phenomenon leads to a growing exhibitionism, which is in turn related to individuals personality teats .use of face book is also influenced by peers and experience with the website

Including general environmental effects in K-factor approximation for rice-distributed VANET channels

© 2014. This paper presents a method of approximating the Rician K-factor based on the instantaneous static environment. The strongest signal propagation paths are resolved in order to determine specular and diffuse powers for approximation. The model is experimentally validated in two different urban areas in New South Wales, Australia. Good agreement between the model and experimental data was obtained over short-range communication links, demonstrating the suitability of the model in urban VANETs. The paper concludes with recommendations for methods to account for vehicles in the simulation and incorporating additional phenomena (such as scattering) in the approximation

IEEHR: Improved Energy Efficient Honeycomb based Routing in MANET for Improving Network Performance and Longevity

In present scenario, efficient energy conservation has been the greatest focus in Mobile Adhoc Networks (MANETs). Typically, the energy consumption rate of dense networks is to be reduced by proper topological management. Honeycomb based model is an efficient parallel computing technique, which can manage the topological structures in a promising manner.  Moreover, discovering optimal routes in MANET is the most significant task, to be considered with energy efficiency. With that motive, this paper presents a model called Improved Energy Efficient Honeycomb based Routing (IEEHR) in MANET. The model combines the Honeycomb based area coverage with Location-Aided Routing (LAR), thereby reducing the broadcasting range during the process of path finding. In addition to optimal routing, energy has to be effectively utilized in MANET, since the mobile nodes have energy constraints. When the energy is effectively consumed in a network, the network performance and the network longevity will be increased in respective manner. Here, more amount of energy is preserved during the sleeping state of the mobile nodes, which are further consumed during the process of optimal routing. The designed model has been implemented and analyzed with NS-2 Network Simulator based on the performance factors such as Energy Efficiency, Transmission Delay, Packet Delivery Ratio and Network Lifetime

An Intrusion Detection Using Machine Learning Algorithm Multi-Layer Perceptron (MlP): A Classification Enhancement in Wireless Sensor Network (WSN)

During several decades, there has been a meteoric rise in the development and use of cutting-edge technology. The Wireless Sensor Network (WSN) is a groundbreaking innovation that relies on a vast network of individual sensor nodes. The sensor nodes in the network are responsible for collecting data and uploading it to the cloud. When networks with little resources are deployed harshly and without regulation, security risks occur. Since the rate at which new information is being generated is increasing at an exponential rate, WSN communication has become the most challenging and complex aspect of the field. Therefore, WSNs are insecure because of this. With so much riding on WSN applications, accuracy in replies is paramount. Technology that can swiftly and continually analyse internet data streams is essential for spotting breaches and assaults. Without categorization, it is hard to simultaneously reduce processing time while maintaining a high level of detection accuracy. This paper proposed using a Multi-Layer Perceptron (MLP) to enhance the classification accuracy of a system. The proposed method utilises a feed-forward ANN model to generate a mapping for the training and testing datasets using backpropagation. Experiments are performed to determine how well the proposed MLP works. Then, the results are compared to those obtained by using the Hoeffding adaptive tree method and the Restricted Boltzmann Machine-based Clustered-Introduction Detection System. The proposed MLP achieves 98% accuracy, which is higher than the 96.33% achieved by the RBMC-IDS and the 97% accuracy achieved by the Hoeffding adaptive tree

Explainable deep learning approach for multilabel classification of antimicrobial resistance with missing labels

Predicting Antimicrobial Resistance (AMR) from genomic sequence data has become a significant component of overcoming the AMR challenge, especially given its potential for facilitating more rapid diagnostics and personalised antibiotic treatments. With the recent advances in sequencing technologies and computing power, deep learning models for genomic sequence data have been widely adopted to predict AMR more reliably and error-free. There are many different types of AMR; therefore, any practical AMR prediction system must be able to identify multiple AMRs present in a genomic sequence. Unfortunately, most genomic sequence datasets do not have all the labels marked, thereby making a deep learning modelling approach challenging owing to its reliance on labels for reliability and accuracy. This paper addresses this issue by presenting an effective deep learning solution, Mask-Loss 1D convolution neural network (ML-ConvNet), for AMR prediction on datasets with many missing labels. The core component of ML- ConvNet utilises a masked loss function that overcomes the effect of missing labels in predicting AMR. The proposed ML-ConvNet is demonstrated to outperform state-of-the-art methods in the literature by 10.5%, according to the F1 score. The proposed model’s performance is evaluated using different degrees of the missing label and is found to outperform the conventional approach by 76% in the F1 score when 86.68% of labels are missing. Furthermore, the ML-ConvNet was established with an explainable artificial intelligence (XAI) pipeline, thereby making it ideally suited for hospital and healthcare settings, where model interpretability is an essential requirement

Identification and validation of Triamcinolone and Gallopamil as treatments for early COVID-19 via an in silico repurposing pipeline

SARS-CoV-2, the causative virus of COVID-19 continues to cause an ongoing global pandemic. Therapeutics are still needed to treat mild and severe COVID-19. Drug repurposing provides an opportunity to deploy drugs for COVID-19 more rapidly than developing novel therapeutics. Some existing drugs have shown promise for treating COVID-19 in clinical trials. This in silico study uses structural similarity to clinical trial drugs to identify two drugs with potential applications to treat early COVID-19. We apply in silico validation to suggest a possible mechanism of action for both. Triamcinolone is a corticosteroid structurally similar to Dexamethasone. Gallopamil is a calcium channel blocker structurally similar to Verapamil. We propose that both these drugs could be useful to treat early COVID-19 infection due to the proximity of their targets within a SARS-CoV-2-induced protein-protein interaction network to kinases active in early infection, and the APOA1 protein which is linked to the spread of COVID-19.Comment: 32 pages, 4 figure

Mapping the evidence of the effects of environmental factors on the prevalence of antibiotic resistance in the non-built environment: Protocol for a systematic evidence map

Background: Human, animal, and environmental health are increasingly threatened by the emergence and spread of antibiotic resistance. Inappropriate use of antibiotic treatments commonly contributes to this threat, but it is also becoming apparent that multiple, interconnected environmental factors can play a significant role. Thus, a One Health approach is required for a comprehensive understanding of the environmental dimensions of antibiotic resistance and inform science-based decisions and actions. The broad and multidisciplinary nature of the problem poses several open questions drawing upon a wide heterogeneous range of studies. Objective: This study seeks to collect and catalogue the evidence of the potential effects of environmental factors on the abundance or detection of antibiotic resistance determinants in the outdoor environment, i.e., antibiotic resistant bacteria and mobile genetic elements carrying antibiotic resistance genes, and the effect on those caused by local environmental conditions of either natural or anthropogenic origin. Methods: Here, we describe the protocol for a systematic evidence map to address this, which will be performed in adherence to best practice guidelines. We will search the literature from 1990 to present, using the following electronic databases: MEDLINE, Embase, and the Web of Science Core Collection as well as the grey literature. We shall include full-text, scientific articles published in English. Reviewers will work in pairs to screen title, abstract and keywords first and then full-text documents. Data extraction will adhere to a code book purposely designed. Risk of bias assessment will not be conducted as part of this SEM. We will combine tables, graphs, and other suitable visualisation techniques to compile a database i) of studies investigating the factors associated with the prevalence of antibiotic resistance in the environment and ii) map the distribution, network, cross-disciplinarity, impact and trends in the literature.This work was supported by funding from the European Union’s Horizon 2020 Research and Innovation programme under grant agreement No 773830: One Health European Joint Programme. The funder had no role in the development of this protocol.info:eu-repo/semantics/publishedVersio

Phase Formation, Thermal Stability and Mechanical Properties of a Cu-Al-Ni-Mn Shape Memory Alloy Prepared by Selective Laser Melting

Selective laser melting (SLM) is an additive manufacturing process used to produce parts with complex geometries layer by layer. This rapid solidification method allows fabricating samples in a non-equilibrium state and with refined microstructure. In this work, this method is used to fabricate 3 mm diameter rods of a Cu-based shape memory alloy. The phase formation, thermal stability and mechanical properties were investigated and correlated. Samples with a relative density higher than 92% and without cracks were obtained. A single monoclinic martensitic phase was formed with average grain size ranging between 28 to 36 μm. The samples exhibit a reverse martensitic transformation temperature around 106 ± 2 °C and a large plasticity in compression (around 15±1%) with a typical “double-yielding” behaviour