85 research outputs found
Dynamic Super Round Based Distributed Task Scheduling for UAV Networks
No full textNetworks of Unmanned Aerial Vehicles (UAVs) are emerging in many application domains, e.g., military surveillance. To perform collaborative tasks, the involved UAVs exchange several types of information, e.g., sensor data and commands. The major question here is how to schedule the tasks under dynamic traffic flows to provide network services. Existing solutions use the Round-Robin Strategy (RRS), where the tasks are scheduled statistically by dividing the time into fixed-length rounds. However, the RRS wastes significant network and device resources due to task scheduling in each round. This paper proposes DROVE – a novel clustering approach that allows the UAVs for dynamic task scheduling. However, determining the task scheduling is crucial, as it significantly affects several network parameters, e.g., throughput. Therefore, we devise the problem of distributed task scheduling under dynamic traffic flow scenarios to optimize the throughput. We propose a clustering task scheduling algorithm to serve dynamic traffic flows. Particularly, we integrate the dynamic traffic flows into the Lyapunov drift analysis framework, and determine the throughput optimality of our proposed scheduling algorithm. We perform extensive simulations to validate the effectiveness of DROVE. The results show that DROVE outperforms the state-of-the-art solutions in terms of energy consumption, clustering overhead, throughput, end-to-end delay, flow success rate and packet drop rate. </p
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No full textThe Ca2+-sensor synaptotagmin-1 that triggers neuronal exocytosis binds to negatively charged membrane lipids (mainly phosphatidylserine (PtdSer) and phosphoinositides (Ptdlns)) but the molecular details of this process are not fully understood. Using quantitative thermodynamic, kinetic and structural methods, we show that synaptotagmin-1 (from Rattus norvegicus and expressed in Escherichia coli) binds to Ptdlns(4,5)P-2 via a polybasic lysine patch in the C2B domain, which may promote the priming or docking of synaptic vesicles. Ca2+ neutralizes the negative charges of the Ca2+-binding sites, resulting in the penetration of synaptotagmin-1 into the membrane, via binding of PtdSer, and an increase in the affinity of the polybasic lysine patch to phosphatidylinositol-4,5-bisphosphate (PtdIns(4,5)P-2). These Ca2+-induced events decrease the dissociation rate of synaptotagmin-1 membrane binding while the association rate remains unchanged. We conclude that both membrane penetration and the increased residence time of synaptotagmin-1 at the plasma membrane are crucial for triggering exocytotic membrane fusion
FuzzyPPI: Human Proteome at Fuzzy Semantic Space
No full textLarge scale protein-protein interaction (PPI) network of an organism provides key insights into the cellular and molecular functionalities, signaling pathways and underlying disease mechanisms. If we consider the complete interactome of any given organism, the total number of unexplored protein interactions significantly outnumbers the known positive and negative interactions. For Human 20,350 reviewed proteins can generate over ~207 million potential interactions. However, the combination of all known PPI datasets, contains only ~5.6 million positive and ~758k negative protein-protein interactions (NPPI), that together is ~3.1% what is more, conventional PPI prediction methods produce binary results. At the same time recent studies show that protein binding affinities may prove to be effective in detecting protein complexes, disease association analysis, signaling network reconstruction, etc. In this work we present a fuzzy semantic scoring function using the Gene Ontology (GO) graphs to assess the binding affinity between any two proteins at an organism level. We have implemented a distributed algorithm in Apache Spark that computes this function and processed the complete Human PPI network of ~182 million potential interactions resulting from 19,106 reviewed proteins for which GO annotations are available. The quality of the computed scores has been validated with respect to the available state-of-the-art methods on benchmark data sets
Multidimensional Bohr radii for vector-valued holomorphic functions
No full textThe main aim of this paper is to answer certain open questions related to the
exact values of multidimensional Bohr radii by using the concept of arithmetic
Bohr radius for vector-valued holomorphic functions defined in complete
Reinhardt domains in . More precisely, we study the asymptotic
estimates of the arithmetic Bohr radius for holomorphic functions in the unit
ball of spaces with values in arbitrary
complex Banach spaces. Many of our results generalize the results obtained by
Defant, Maestre, and Prengel [Q. J. Math. 59, (2008), pp. 189--205].Comment: We have revised some of proof of our result
Bohr and Rogosinski inequalities for operator valued holomorphic functions
No full textFor any complex Banach space and each , we introduce
the -Bohr radius of order is
defined by \widetilde{R}_{p,N}(X)=\sup \left\{r\geq 0:
\sum_{k=0}^{N}\norm{x_k}^p r^{pk} \leq \norm{f}^p_{H^{\infty}(\mathbb{D},
X)}\right\}, where . Here
denotes the unit disk. We also introduce the following geometric notion of
-uniformly -convexity of order for a complex Banach space
for some . In this paper, for and each
, we prove that a complex Banach space is -uniformly
-convex of order if, and only if, the -Bohr radius of order
. We also study the -Bohr radius of order
for the Lebesgue spaces for or . Finally, we prove an operator valued analogue of a refined version of Bohr
and Rogosinski inequality for bounded holomorphic functions from the unit disk
into , where
denotes the space of all bounded linear operator on a complex Hilbert space
.Comment: 16 page
Composition-Differentiation Operator on Weighted Bergman Spaces
No full textIn this paper, we study the complex symmetry of weighted
composition-differentiation operator on weighted Bergman
spaces with respect to the conjugation
for . We obtain explicit conditions
for which the operator is Hermitian and normal. We also
characterize the complex symmetric weighted composition-differentiation
operator for derivative Hardy spaces.Comment: 14 page
RUBic: rapid unsupervised biclustering
No full textBiclustering of biologically meaningful binary information is essential in many applications related to drug discovery, like protein-protein interactions and gene expressions. However, for robust performance in recently emerging large health datasets, it is important for new biclustering algorithms to be scalable and fast. We present a rapid unsupervised biclustering (RUBic) algorithm that achieves this objective with a novel encoding and search strategy. RUBic significantly reduces the computational overhead on both synthetic and experimental datasets shows significant computational benefits, with respect to several state-of-the-art biclustering algorithms. In 100 synthetic binary datasets, our method took ~71.1s to extract 494,872 biclusters. In the human PPI database of size 4085x4085, our method generates 1840 biclusters in ~48.6s. On a central nervous system embryonic tumor gene expression dataset of size 712,940, our algorithm takes 101 min to produce 747,069 biclusters, while the recent competing algorithms take significantly more time to produce the same result. RUBic is also evaluated on five different gene expression datasets and shows significant speed-up in execution time with respect to existing approaches to extract significant KEGG-enriched bi-clustering. RUBic can operate on two modes, base and flex, where base mode generates maximal biclusters and flex mode generates less number of clusters and faster based on their biological significance with respect to KEGG pathways. The code is available at ( https://github.com/CMATERJU-BIOINFO/RUBic ) for academic use only
Role of rural off-farm employment in earning income and livelihood in the coastal region of West Bengal, India
No full textThe study was conducted in the coastal region of West Bengal, India to document the prevalent farming systems and explore the opportunity of nonfarm activities in generating income and livelihood for the rural households. This paper concentrates in finding out the key determinants of participation in nonfarm income and employment generation activities across rural households. The analytical framework yields different activity choices as optimal solutions to a simple utility maximization problem. The empirical inquiry reveals that education, family size and access to land assets plays major role in accessing more remunerative nonfarm employment. The region is quite underdeveloped such that traditional rural self-employment activities still contributes 30.94 percent of household income and provide employment to 40.71 percent rural household. The number of working men, number of working women, age and education level are the other important determinants of nonfarm activities for the rural households
A survey on Ebola genome and current trends in computational research on the Ebola virus
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