The effects of plants(Typha Latifolia)and root-bed medium on the treatment of domestic sewage within a vertical flow constructed wetland /

The effect that plants {Typha latifolia) as well as root-bed medium physical and chemical characteristics have on the treatment of primary treated domestic wastewater within a vertical flow constructed wetland system was investigated. Five sets of cells, with two cells in each set, were used. Each cell was made of concrete and measured 1 .0 m X 1 .0 m and was 1.3 m deep. Four different root-bed media were tested : Queenston Shale, Fonthill Sand, Niagara Shale and a Michigan Sand. Four of the sets contained plants and a single type of root-bed medium. The influence of plants was tested by operating a Queenston Shale set without plants. Due to budget constraints no replicates were constructed. All of the sets were operated independently and identically for twenty-eight months. Twelve months of data are presented here, collected after 16 months of continuous operation. Root-bed medium type did not influence BOD5 removal. All of the sets consistently met Ontario Ministry of Environment (MOE) requirements (<25 mg/L) for BOD5 throughout the year. The 12 month average BOD5 concentration from all sets with plants was below 2.36 mg/L. All of the sets were within MOE discharge requirements (< 25 mg/L) for suspended solids with set effluent concentrations ranging from 1.53 to 14.80 mg/L. The Queenston Shale and Fonthill Sand media removed the most suspended solids while the Niagara Shale set produced suspended solids. The set containing Fonthill Sand was the only series to meet MOE discharge requirements (< Img/L) for total phosphorus year-round with a twelve month mean effluent concentration of 0.23 mg/L. Year-round all of the root-bed media were well below MOE discharge requirements (< 20mg/L in winter and < 10 mg/L in sumnner) for ammonium. The Queenston Shale and Fonthill Sand sets removed the most total nitrogen. Plants had no effect on total nitrogen removal, but did influence how nitrogen was cycled within the system. Plants increased the removal of suspended solids by 14%, BOD5 by 10% and total phosphorus by 22%. Plants also increased the amount of dissolved oxygen that entered the system. During the plant growing season removal of total phosphorus was better in all sets with plants regardless of media type. The sets containing Queenston Shale and Fonthill Sand media achieved the best results and plants in the Queenston Shale set increased treatment efficiency for every parameter except nitrogen. Vertical flow wetland sewage treatment systems can be designed and built to consistently meet MOE discharge requirements year-round for BOD5, suspended solids, total phosphorus and ammonium. This system Is generally superior to the free water systems and sub-surface horizontal flow systems in cold climate situations.

Square Root Finding In Graphs

Abstract: Root and root finding are concepts familiar to most branches of mathematics. In graph theory, H is a square root of G and G is the square of H if two vertices x,y have an edge in G if and only if x,y are of distance at most two in H. Graph square is a basic operation with a number of results about its properties in the literature. We study the characterization and recognition problems of graph powers. There are algorithmic and computational approaches to answer the decision problem of whether a given graph is a certain power of any graph. There are polynomial time algorithms to solve this problem for square of graphs with girth at least six while the NP-completeness is proven for square of graphs with girth at most four. The girth-parameterized problem of root fining has been open in the case of square of graphs with girth five. We settle the conjecture that recognition of square of graphs with girth 5 is NP-complete. This result is providing the complete dichotomy theorem for square root finding problem.

The analysis of Metarhizium robertsii potential as endophytic plant root coloniser, plant growth enhancer and antagonist to bean root pathogen Fusarium solani f. sp. phaseolis.

The soil-inhabiting insect-pathogenic fungus Metarhizium robertsii also colonizes plant roots endophytically, thus showing potential as a plant symbiont. M robertsii is not randomly distributed in soils but preferentially associates with the plant rhizosphere when applied in agricultural settings. Root surface and endophytic colonization of switchgrass (Panicum virgatum) and haricot beans (Phaseolus vulgaris) by M robertsii were examined after inoculation with fungal conidia. Light and confocal microscopies were used to ascertain this rhizosphere association. Root lengths, root hair density and emergence of lateral roots were also measured. Initially, M robertsii conidia adhered to, germinated on, and colonized, roots. Furthermore, plant roots treated with Metarhizium grew faster and the density of plant root hairs increased when compared with control plants. The onset of plant root hair proliferation was initiated before germination of M robertsii on the root (within 1-2 days). Plants inoculated with M robertsii AMAD2 (plant adhesin gene) took significantly longer to show root hair proliferation than the wild type. Cell free extracts of M robertsii did not stimulate root hair proliferation. Longer term (60 days) associations showed that M robertsii endophytically colonized individual cortical cells within bean roots. Metarhizium appeared as an amorphous mycelial aggregate within root cortical cells as well as between the intercellular spaces with no apparent damage to the plant. These results suggested that not only is M robertsii rhizosphere competent but displays a beneficial endophytic association with plant roots that results in the proliferation of root hairs. The biocontrol of bean (Phaseolis vulgaris) root rot fungus Fusarium solani f. sp. phaseolis by Metarhizium robertsii was investigated in vitro and in vivo. Dual cultures on Petri dishes showed antagonism of M robertsii against F. solani. A relative inhibition of ca. 60% of F. solani growth was observed in these assays. Cell free culture filtrates of M robertsii inhibited the germination of F. solani conidia by 83% and the inhibitory metabolite was heat stable. Beans plants colonized by M robertsii then exposed to F. solani showed healthier plant profiles and lower disease indices compared to plants not colonized by M robertsii. These results suggested that the insect pathogenic/endophytic fungus M robertsii could also be utilized as a biocontrol agent against certain plant pathogens occurring in the rhizosphere.

Tests of Joint Hypotheses for Time Series Regression with a Unit Root

This Paper Studies Tests of Joint Hypotheses in Time Series Regression with a Unit Root in Which Weakly Dependent and Heterogeneously Distributed Innovations Are Allowed. We Consider Two Types of Regression: One with a Constant and Lagged Dependent Variable, and the Other with a Trend Added. the Statistics Studied Are the Regression \"F-Test\" Originally Analysed by Dickey and Fuller (1981) in a Less General Framework. the Limiting Distributions Are Found Using Functinal Central Limit Theory. New Test Statistics Are Proposed Which Require Only Already Tabulated Critical Values But Which Are Valid in a Quite General Framework (Including Finite Order Arma Models Generated by Gaussian Errors). This Study Extends the Results on Single Coefficients Derived in Phillips (1986A) and Phillips and Perron (1986).

GLS Detrending, Efficient Unit Root Tests and Structural Change

We extend the class of M-tests for a unit root analyzed by Perron and Ng (1996) and Ng and Perron (1997) to the case where a change in the trend function is allowed to occur at an unknown time. These tests M(GLS) adopt the GLS detrending approach of Dufour and King (1991) and Elliott, Rothenberg and Stock (1996) (ERS). Following Perron (1989), we consider two models : one allowing for a change in slope and the other for both a change in intercept and slope. We derive the asymptotic distribution of the tests as well as that of the feasible point optimal tests PT(GLS) suggested by ERS. The asymptotic critical values of the tests are tabulated. Also, we compute the non-centrality parameter used for the local GLS detrending that permits the tests to have 50% asymptotic power at that value. We show that the M(GLS) and PT(GLS) tests have an asymptotic power function close to the power envelope. An extensive simulation study analyzes the size and power in finite samples under various methods to select the truncation lag for the autoregressive spectral density estimator. An empirical application is also provided.

Testing for a Unit Root in Panels with Dynamic Factors

This paper studies testing for a unit root for large n and T panels in which the cross-sectional units are correlated. To model this cross-sectional correlation, we assume that the data is generated by an unknown number of unobservable common factors. We propose unit root tests in this environment and derive their (Gaussian) asymptotic distribution under the null hypothesis of a unit root and local alternatives. We show that these tests have significant asymptotic power when the model has no incidental trends. However, when there are incidental trends in the model and it is necessary to remove heterogeneous deterministic components, we show that these tests have no power against the same local alternatives. Through Monte Carlo simulations, we provide evidence on the finite sample properties of these new tests.

Beyond Panel Unit Root Tests: Using Multiple Testing to Determine the Non Stationarity Properties of Individual Series in a Panel

Most panel unit root tests are designed to test the joint null hypothesis of a unit root for each individual series in a panel. After a rejection, it will often be of interest to identify which series can be deemed to be stationary and which series can be deemed nonstationary. Researchers will sometimes carry out this classification on the basis of n individual (univariate) unit root tests based on some ad hoc significance level. In this paper, we demonstrate how to use the false discovery rate (FDR) in evaluating I(1)=I(0) classifications based on individual unit root tests when the size of the cross section (n) and time series (T) dimensions are large. We report results from a simulation experiment and illustrate the methods on two data sets.

Polymer Nanocomposites: Crystallization, Reinforcement and Conductivity through SWNTs

The current research investigates the possibility of using single walled carbon nanotubes (SWNTs) as filler in polymers to impart several properties to the matrix polymer. SWNTs in a polymer matrix like poly(ethylene terephthalate) induce nucleation in its melt crystallization, provide effective reinforcement and impart electrical conductivity. We adopt a simple melt compounding technique for incorporating the nanotubes into the polymer matrix. For attaining a better dispersion of the filler, an ultrasound assisted dissolution-evaporation method has also been tried. The resulting enhancement in the materials properties indicates an improved disentanglement of the nanotube ropes, which in turn provides effective matrix-filler interaction. PET-SWNT nanocomposite fibers prepared through melt spinning followed by subsequent drawing are also found to have significantly higher mechanical propertiesas compared to pristine PET fiber.SWNTs also find applications in composites based on elastomers such as natural rubber as they can impart electrical conductivity with simultaneous improvement in the mechanical properties.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.