Frequency of Intestinal Parasites in Stool Samples

Background: To study the frequency of intestinal parasites in the stool specimens Method: In this cross sectional study, 643 samples of stool were collected. Each stool sample was analyzed grossly and microscopically. Results: The highest prevalent infections were of the protozoan (84.9%). Entamoeba histolytica(63.5%), followed by Giardia lamblia(1.4% were the most prevalent. The cases of helminthiasis (Ascaris lumbricoides, Hymenolepis nana, Ankylostoma duodenale, Enterobius vermicularis and Taenia Saginata) came out to be 14.9%. Conclusion: Protozoal infections due to Entamoeba histolytica and Giardia lamblia are much more prevalent than the helminthiasis . The prevalence of intestinal parasitic infections was higher in males (62%) than females (38%)

Malicious AODV: implementation and analysis of routing attacks in MANETs

From the security perspective Mobile Ad hoc Networks (MANETs) are amongst the most challenging research areas and one of the key reasons for this is the ambiguous nature of insider attacks in these networks. In recent years, many attempts have been made to study the intrinsic attributes of these insider attacks but the focus has generally been on the analysis of one or very few particular attacks, or only the survey of various attacks without any performance analysis. Therefore, a major feature that research has lately lacked is a detailed and comprehensive study of the effects of various insider attacks on the overall performance of MANETs. In this paper we investigate, in detail, some of the most severe attacks against MANETs namely the blackhole attack, sinkhole attack, selfish node behavior, RREQ flood, hello flood, and selective forwarding attack. A detailed NS-2 implementation of launching these attacks successfully using Ad hoc On-Demand Distance Vector (AODV) routing protocol has been presented and a comprehensive and comparative analysis of these attacks is performed. We use packet efficiency, routing overhead, and throughput as our performance metrics. Our simulation-based study shows that flooding attacks like RREQ flood and hello flood drastically increase the routing overhead of the protocol. Route modification attacks such as sinkhole and blackhole are deadly and severely affect the packet efficiency and bring down the throughput to unacceptable ranges

Materialized view selection for aggregate view recommendation

Data analysts arduously rely on data visualizations for drawing insights into huge and complex datasets. However, finding interesting visualizations by manually specifying various parameters such as type, attributes, granularity is a protracted process. Simplification of this process requires systems that can automatically recommend interesting visualizations. Such systems primarily work first by evaluating the utility of all possible visualizations and then recommending the top-k visualizations to the user. However, this process is achieved at the hands of high data processing cost. That cost is further aggravated by the presence of numerical dimensional attributes, as it requires binned aggregations. Therefore, there is a need of recommendation systems that can facilitate data exploration tasks with the increased efficiency, without compromising the quality of recommendations. The most expensive operation while computing the utility of the views is the time spent in executing the query related to the views. To reduce the cost of this particular operation, we propose a novel technique mView, which instead of answering each query related to a view from scratch, reuses results of the already executed queries. This is done by incremental materialization of a set of views in optimal order and answering the queries from the materialized views instead of the base table. The experimental evaluation shows that the mView technique can reduce the cost at least by 25–30% as compared to the previously proposed methods

Efficient recommendation of aggregate data visualizations

Data visualization is a common and effective technique for data exploration. However, for complex data, it is infeasible for an analyst to manually generate and browse all possible visualizations for insights. This observation motivated the need for automated solutions that can effectively recommend such visualizations. The main idea underlying those solutions is to evaluate the utility of all possible visualizations and then recommend the top-k visualizations. This process incurs high data processing cost, that is further aggravated by the presence of numerical dimensional attributes. To address that challenge, we propose novel view recommendation schemes, which incorporate a hybrid multi-objective utility function that captures the impact of numerical dimension attributes. Our first scheme, Multi-Objective View Recommendation for Data Exploration (MuVE), adopts an incremental evaluation of our multi-objective utility function, which allows pruning of a large number of low-utility views and avoids unnecessary objective evaluations. Our second scheme, upper MuVE (uMuVE), further improves the pruning power by setting the upper bounds on the utility of views and allowing interleaved processing of views, at the expense of increased memory usage. Finally, our third scheme, Memory-aware uMuVE (MuMuVE), provides pruning power close to that of uMuVE, while keeping memory usage within a pre-specified limit

An End-to-End QoS Mechanism for Grid Bulk Data Transfer for Supporting Virtualization

Abstract. We consider sustainable and deterministic QoS a key ingredient for providing virtualization and hence introduce an end-to-end Quality of Service mechanism for Grid bulk data transfers. Our mechanism enables per-flow guarantees and efficiently utilizes available resources without requiring any router support except for the provisioning of a single high class traffic aggregate. This is attained by taking the specific requirements and environment conditions in common Grids into account. We document simulation results which illustrate how guarantees are realized by applying admission control, and by uniformly using a max-min fair congestion control mechanism for all flows

Frequency of Intestinal Parasites in Stool Samples

Background: To study the frequency of intestinal parasites in the stool specimens Method: In this cross sectional study, 643 samples of stool were collected. Each stool sample was analyzed grossly and microscopically. Results: The highest prevalent infections were of the protozoan (84.9%). Entamoeba histolytica(63.5%), followed by Giardia lamblia(1.4% were the most prevalent. The cases of helminthiasis (Ascaris lumbricoides, Hymenolepis nana, Ankylostoma duodenale, Enterobius vermicularis and Taenia Saginata) came out to be 14.9%. Conclusion: Protozoal infections due to Entamoeba histolytica and Giardia lamblia are much more prevalent than the helminthiasis . The prevalence of intestinal parasitic infections was higher in males (62%) than females (38%)