QoS Constrained Optimal Sink and Relay Placement in Planned Wireless Sensor Networks

We are given a set of sensors at given locations, a set of potential locations for placing base stations (BSs, or sinks), and another set of potential locations for placing wireless relay nodes. There is a cost for placing a BS and a cost for placing a relay. The problem we consider is to select a set of BS locations, a set of relay locations, and an association of sensor nodes with the selected BS locations, so that number of hops in the path from each sensor to its BS is bounded by hmax, and among all such feasible networks, the cost of the selected network is the minimum. The hop count bound suffices to ensure a certain probability of the data being delivered to the BS within a given maximum delay under a light traffic model. We observe that the problem is NP-Hard, and is hard to even approximate within a constant factor. For this problem, we propose a polynomial time approximation algorithm (SmartSelect) based on a relay placement algorithm proposed in our earlier work, along with a modification of the greedy algorithm for weighted set cover. We have analyzed the worst case approximation guarantee for this algorithm. We have also proposed a polynomial time heuristic to improve upon the solution provided by SmartSelect. Our numerical results demonstrate that the algorithms provide good quality solutions using very little computation time in various randomly generated network scenarios

An Experimental Study of Variable Compression Ratio Engine Using Diesel Blend - A Computing Approach

Increase in the scarcity of the fossil fuels, prices and global warming have generated an interest in developing alternate fuel for engine. Technologies now focusing on development of plant based fuel, plant oils and plant fats as alternative fuel. The present work deals with finding the better compression ratio for the honne oil diesel blend fueled C.I engine at variable load and constant speed operation. In order to find out optimum compression ratio, experiments are carried out on a single cylinder four stroke variable compression ratio diesel engine. Engine performance tests are carried out at different compression ratio values. The optimum compression ratio that gives better engine performance is found from the experimental results. Using experimental data Artificial Neural Network (ANN) model was developed and the values were predicted using ANN. Finally the predicted values were validated with the experimentally

Harmonic Nature of Maddalam : - A Study

 The sound samples of different strokes of maddalam are analysed using MIR toolbox. The frequency spectrum, attack and decay parameters are studied. The reasons for the harmonic nature of maddalam are identified

Robotic cloth manipulation for clothing assistance task using Dynamic Movement Primitives

The need of robotic clothing assistance in the field of assistive robotics is growing, as it is one of the most basic and essential assistance activities in daily life of elderly and disabled people. In this study we are investigating the applicability of using Dynamic Movement Primitives (DMP) as a task parameterization model for performing clothing assistance task. Robotic cloth manipulation task deals with putting a clothing article on both the arms. Robot trajectory varies significantly for various postures and also there can be various failure scenarios while doing cooperative manipulation with non-rigid and highly deformable clothing article. We have performed experiments on soft mannequin instead of human. Result shows that DMPs are able to generalize movement trajectory for modified posture.3rd International Conference of Robotics Society of India (AIR \u2717: Advances in Robotics), June 28 - July 2, 2017, New Delhi, Indi

A Graph-Based Context-Aware Model to Understand Online Conversations

Online forums that allow for participatory engagement between users have been transformative for the public discussion of many important issues. However, such conversations can sometimes escalate into full-blown exchanges of hate and misinformation. Existing approaches in natural language processing (NLP), such as deep learning models for classification tasks, use as inputs only a single comment or a pair of comments depending upon whether the task concerns the inference of properties of the individual comments or the replies between pairs of comments, respectively. But in online conversations, comments and replies may be based on external context beyond the immediately relevant information that is input to the model. Therefore, being aware of the conversations' surrounding contexts should improve the model's performance for the inference task at hand. We propose GraphNLI, a novel graph-based deep learning architecture that uses graph walks to incorporate the wider context of a conversation in a principled manner. Specifically, a graph walk starts from a given comment and samples "nearby" comments in the same or parallel conversation threads, which results in additional embeddings that are aggregated together with the initial comment's embedding. We then use these enriched embeddings for downstream NLP prediction tasks that are important for online conversations. We evaluate GraphNLI on two such tasks - polarity prediction and misogynistic hate speech detection - and found that our model consistently outperforms all relevant baselines for both tasks. Specifically, GraphNLI with a biased root-seeking random walk performs with a macro-F1 score of 3 and 6 percentage points better than the best-performing BERT-based baselines for the polarity prediction and hate speech detection tasks, respectively.Comment: 25 pages, 9 figures. arXiv admin note: text overlap with arXiv:2202.0817

Who has the last word? Understanding How to Sample Online Discussions

In online debates individual arguments support or attack each other, leading to some subset of arguments being considered more relevant than others. However, in large discussions readers are often forced to sample a subset of the arguments being put forth. Since such sampling is rarely done in a principled manner, users may not read all the relevant arguments to get a full picture of the debate. This paper is interested in answering the question of how users should sample online conversations to selectively favour the currently justified or accepted positions in the debate. We apply techniques from argumentation theory and complex networks to build a model that predicts the probabilities of the normatively justified arguments given their location in online discussions. Our model shows that the proportion of replies that are supportive, the number of replies that comments receive, and the locations of un-replied comments all determine the probability that a comment is a justified argument. We show that when the degree distribution of the number of replies is homogeneous along the discussion, for acrimonious discussions, the distribution of justified arguments depends on the parity of the graph level. In supportive discussions the probability of having justified comments increases as one moves away from the root. For discussion trees that have a non-homogeneous in-degree distribution, for supportive discussions we observe the same behaviour as before, while for acrimonious discussions we cannot observe the same parity-based distribution. This is verified with data obtained from the online debating platform Kialo. By predicting the locations of the justified arguments in reply trees, we can suggest which arguments readers should sample to grasp the currently accepted opinions in such discussions. Our models have important implications for the design of future online debating platforms

Stochastic free vibration analysis of RC buildings

Background/Objectives: Free vibration response of RC structures is random in nature due to the uncertainties exist in geometry, material properties and loading. Stochastic analysis methods can represent this randomness in responses. Methods: The Monte Carlo Simulation is a widely accepted method for stochastic structural analysis but the computational effort and cost associated with it is a limitation and hence in the present study, it is used as a method for the comparison and verification of the results obtained by other metamodel based approaches such as the response surface method. The number of analysis samples required depends on the type of approach adopted. Findings: Three different design of experiments approaches, Central Composite Design, Box Behnken Design and Full Factorial Design, where used in response surface modelling. The present study is an evaluation of these metamodel based approaches. The natural frequencies obtained by these methods of analysis were comparable with the results from Monte Carlo Simulation. However, the latter required one million analyses, making it computationally cumbersome. The Central Composite Design proved to be the most efficient method as it yielded the most accurate results even though the number of runs were marginally more than the 62 required for Box Behnken Design. Improvements: These response surface based metamodel approaches can be further applied to nonlinear stochastic analysis of structures where the cost and effort of analysis is significantly highe

A COMPARATIVE STUDY ON THE IN-VITRO ANTIMICROBIAL ACTIVITY OF THE ROOTS OF FOUR THOTTEA SPECIES

Objective: The main objective of the present study was to investigate the antimicrobial activity of the methanol extract of the roots of four Thottea species. Methods: The root extracts of four Thottea species were subjected to antimicrobial assay by Minimum Inhibitory Concentration (MIC) and Agar Disc diffusion Assay against various medically important pathogens. Results: It is evident from the study that. Significant antibacterial activity was recorded by Thottea sivarajanii and highest activity was recorded against Pseudomonas aeruginosa and Staphylococcus epidermis (64 µg/ml). Out of the four extracts tested for antifungal activity, Thottea barberi and Thottea ponmudiana recorded significant antifungal activity and the highest activity was recorded by T. barberi against Trichophyton rubrum (16µg/ml). Conclusion: Results offer a scientific basis for the traditional use of Thottea species in the treatment of microbial infections, showing that the plant extract has an enormous potential as a prospective alternative drug against microbial pathogens. The present study lays the basis for future studies, to validate the possible use of Thottea species as a candidate in the treatment of microbial infections

Reactive oxygen species (ROS) mediated enhanced anti-candidal activity of ZnS-ZnO nanocomposites with low inhibitory concentrations

Enhanced antifungal activity against the yeast species Candida albicans, Candida tropicalis and Saccharomyces cerevisiae was displayed by ZnS-ZnO nanocomposites prepared by a simple precipitation technique. The antifungal activity was significantly more in the presence of indoor light than under dark conditions and was a clear confirmation of the inhibitory role of reactive oxygen species (ROS) generated in situ by the photocatalytic nanocomposites. The generation of ROS was further evidenced by flow cytometry results and membrane permeabilisation studies. Time kill assay and growth curve analysis indicated diminished antifungal activity under dark conditions due primarily to Zn2+ efflux in solution. © 2015 The Royal Society of Chemistry

Long-Term Cardiovascular Diseases of Heatstroke: A Delayed Pathophysiology Outcome.

Heatstroke, defined as an elevated core body temperature above 40°C accompanied by altered mental status (e.g., confusion, disorientation, seizure and coma), is the most severe and life-threatening condition in the spectrum of heat-related illnesses. Heatstroke patients may present with multi-organ dysfunction, but with rapid cooling and organ failure management, a full recovery often occurs within weeks. Long-term impairment is rare, with neurological impairment occurring most frequently. Despite an abundance of research on the persistent neurological and hepatic impairments, our knowledge of the long-term cardiovascular events in patients with heatstroke history is poor. We wondered whether heatstroke leads to cardiovascular diseases long after full recovery. Using Pubmed, Web of Science and Scopus, we gathered cohort studies looking at cardiovascular disease incidence or mortality as an outcome, including heatstroke animal studies. Based on the available literature, we found that a history of heatstroke is associated with an increased risk of cardiovascular diseases, including ischemic heart disease, heart failure and atrial fibrillation. Delayed metabolic disturbances occurring in exertional heatstroke mice are linked to the formation of atherosclerosis and the development of heart failure. These processes provide potential pathophysiological pathways leading to ischemic heart disease and heart failure in heatstroke patients. Our findings may massively impact our understanding of heatstroke recovery and the follow up of heatstroke patients. Therefore larger, more adequately powered cohort studies with cardiovascular disease as an outcome, in tandem with animal studies examining the underlying pathophysiology, are required to confirm or reject these findings and answer the proposed questions