A study of dynamical syn-eco symbiotic system with bio-economic aspect at interior equilibrium

This paper proposes a multi species ecosystem with optimal harvesting of the prey (S1), and Super predator (S3).The predator (S2) is surviving on the prey and the super predator is surviving on predator. The mathematical model is defined by three non-linear ordinary differential equations .The existence of bionomic equilibrium of the system has been discussed. The Local stability is discussed using Routh-Hurwitz‘s criteria and global stability by Lyapunov’s function. The analytical stability criteria are supported by numerical simulations using Mat lab

Two-Hop Routing with Traffic-Differentiation for QoS Guarantee in Wireless Sensor Networks

This paper proposes a Traffic-Differentiated Two-Hop Routing protocol for Quality of Service (QoS) in Wireless Sensor Networks (WSNs). It targets WSN applications having different types of data traffic with several priorities. The protocol achieves to increase Packet Reception Ratio (PRR) and reduce end-to-end delay while considering multi-queue priority policy, two-hop neighborhood information, link reliability and power efficiency. The protocol is modular and utilizes effective methods for estimating the link metrics. Numerical results show that the proposed protocol is a feasible solution to addresses QoS service differenti- ation for traffic with different priorities.Comment: 13 page

What Can We Learn Privately?

Learning problems form an important category of computational tasks that generalizes many of the computations researchers apply to large real-life data sets. We ask: what concept classes can be learned privately, namely, by an algorithm whose output does not depend too heavily on any one input or specific training example? More precisely, we investigate learning algorithms that satisfy differential privacy, a notion that provides strong confidentiality guarantees in contexts where aggregate information is released about a database containing sensitive information about individuals. We demonstrate that, ignoring computational constraints, it is possible to privately agnostically learn any concept class using a sample size approximately logarithmic in the cardinality of the concept class. Therefore, almost anything learnable is learnable privately: specifically, if a concept class is learnable by a (non-private) algorithm with polynomial sample complexity and output size, then it can be learned privately using a polynomial number of samples. We also present a computationally efficient private PAC learner for the class of parity functions. Local (or randomized response) algorithms are a practical class of private algorithms that have received extensive investigation. We provide a precise characterization of local private learning algorithms. We show that a concept class is learnable by a local algorithm if and only if it is learnable in the statistical query (SQ) model. Finally, we present a separation between the power of interactive and noninteractive local learning algorithms.Comment: 35 pages, 2 figure

Novel Organotin(IV)-Schiff Base Complexes: Synthesis, Characterization, Antimicrobial Activity, and DNA Interaction Studies

Four organotin(IV) complexes with 2-(2-hydroxybenzylideneamino)isoindoline-1,3-dione (L1), and 4-(4-hydroxy-3-methoxybenzylideneamino-N-(pyrimidin-2-yl)benzenesulfonamide (L2) were synthesized and well characterized by analytical and spectral studies. The synthesized compounds were tested for antimicrobial activity by disc diffusion method. The DNA binding of the complexes 1 and 3 with CT-DNA has been performed with absorption spectroscopy, which showed that both the complexes are avid binders of CT-DNA. Also the nuclease activity of complexes 1 and 3 with plasmid DNA (pUC19) was studied using agarose gel electrophoresis. The complex 1 can act as effective DNA cleaving agent when compared to complex 3 resulting in the nicked form of DNA under physiological conditions. The gel was run both in the absence and presence of the oxidizing agent

The FastMap Algorithm for Shortest Path Computations

We present a new preprocessing algorithm for embedding the nodes of a given edge-weighted undirected graph into a Euclidean space. The Euclidean distance between any two nodes in this space approximates the length of the shortest path between them in the given graph. Later, at runtime, a shortest path between any two nodes can be computed with A* search using the Euclidean distances as heuristic. Our preprocessing algorithm, called FastMap, is inspired by the data mining algorithm of the same name and runs in near-linear time. Hence, FastMap is orders of magnitude faster than competing approaches that produce a Euclidean embedding using Semidefinite Programming. FastMap also produces admissible and consistent heuristics and therefore guarantees the generation of shortest paths. Moreover, FastMap applies to general undirected graphs for which many traditional heuristics, such as the Manhattan Distance heuristic, are not well defined. Empirically, we demonstrate that A* search using the FastMap heuristic is competitive with A* search using other state-of-the-art heuristics, such as the Differential heuristic

Imaging Studies of photodamage and self-healing in disperse orange 11 dye-doped PMMA

We report on optical imaging studies of self-healing after laser-induced photodamage in disperse orange 11 dye doped into poly(methyl methacrylate) (PMMA) polymer. In particular, the high spatial-contrast image of the damage track made by a line focus pump laser allows the recovery rates to be measured as a function of burn dose using the relationship between transverse distance and pump intensity profile. The time evolution of the damaged population results in an intensity-independent time constant of {\tau} = 490\pm23 min, in agreement with independent measurements of the time evolution of amplified spontaneous emission. Also observed is a damage threshold above which the material does not fully recover.Comment: 5 pages, 8 figure