Treatment of landfill leachate with different techniques: An overview

Landfill leachate is characterised by high chemical and biological oxygen demand and generally consists of undesirable substances such as organic and inorganic contaminants. Landfill leachate may differ depending on the content and age of landfill contents, the degradation procedure, climate and hydrological conditions. We aimed to explain the characteristics of landfill leachate and define the practicality of using different techniques for treating landfill leachate. Different treatments comprising biological methods (e.g. bioreactors, bioremediation and phytoremediation) and physicochemical approaches (e.g. advanced oxidation processes, adsorption, coagulation/ flocculation and membrane filtration) were investigated in this study. Membrane bioreactors and integrated biological techniques, including integrated anaerobic ammonium oxidation and nitrification/denitrification processes, have demonstrated high performance in ammonia and nitrogen elimination, with a removal effectiveness of more than 90%. Moreover, improved elimination efficiency for suspended solids and turbidity has been achieved by coagulation/ flocculation techniques. In addition, improved elimination of metals can be attained by combining different treatment techniques, with a removal effectiveness of 40–100%. Furthermore, combined treatment techniques for treating landfill leachate, owing to its high chemical oxygen demand and concentrations of ammonia and low biodegradability, have been reported with good performance. However, further study is necessary to enhance treatment methods to achieve maximum removal efficiency

Emergence of Order in Textured Patterns

A characterization of textured patterns, referred to as the disorder function \bar\delta(\beta), is used to study properties of patterns generated in the Swift-Hohenberg equation (SHE). It is shown to be an intensive, configuration-independent measure. The evolution of random initial states under the SHE exhibits two stages of relaxation. The initial phase, where local striped domains emerge from a noisy background, is quantified by a power law decay \bar\delta(\beta) \sim t^{-{1/2} \beta}. Beyond a sharp transition a slower power law decay of \bar\delta(\beta), which corresponds to the coarsening of striped domains, is observed. The transition between the phases advances as the system is driven further from the onset of patterns, and suitable scaling of time and \bar\delta(\beta) leads to the collapse of distinct curves. The decay of $\bar\delta(\beta)$ during the initial phase remains unchanged when nonvariational terms are added to the underlying equations, suggesting the possibility of observing it in experimental systems. In contrast, the rate of relaxation during domain coarsening increases with the coefficient of the nonvariational term.Comment: 9 Pages, 8 Postscript Figures, 3 gif Figure

The Phase Structure of an SU(N) Gauge Theory with N_f Flavors

We investigate the chiral phase transition in SU(N) gauge theories as the number of quark flavors, $N_f$, is varied. We argue that the transition takes place at a large enough value of $N_f$ so that it is governed by the infrared fixed point of the $\beta$ function. We study the nature of the phase transition analytically and numerically, and discuss the spectrum of the theory as the critical value of $N_f$ is approached in both the symmetric and broken phases. Since the transition is governed by a conformal fixed point, there are no light excitations on the symmetric side. We extend previous work to include higher order effects by developing a renormalization group estimate of the critical coupling.Comment: 34 pages, 1 figure. More references adde

A novel switching delayed PSO algorithm for estimating unknown parameters of lateral flow immunoassay

In this paper, the parameter identification problem of the lateral flow immunoassay (LFIA) devices is investigated via a new switching delayed particle swarm optimization (SDPSO) algorithm. By evaluating an evolutionary factor in each generation, the velocity of the particle can adaptively adjust the model according to a Markov chain in the proposed SDPSO method. During the iteration process, the SDPSO can adaptively select the inertia weight, acceleration coefficients, locally best particle pbest and globally best particle gbest in the swarm. It is worth highlighting that the pbest and the gbest can be randomly selected from the corresponding values in the previous iteration. That is, the delayed information of the pbest and the gbest can be exploited to update the particle’s velocity in current iteration according to the evolutionary states. The strategy can not only improve the global search but also enhance the possibility of eventually reaching the gbest. The superiority of the proposed SDPSO is evaluated on a series of unimodal and multimodal benchmark functions. Results demonstrate that the novel SDPSO algorithm outperforms some well-known PSO algorithms in aspects of global search and efficiency of convergence. Finally, the novel SDPSO is successfully exploited to estimate the unknown time-delay parameters of a class of nonlinear state-space LFIA model.This work was supported in part by the Royal Society of the U.K., the Alexander von Humboldt Foundation of Germany, the Natural Science Foundation of China under Grant 61403319, the Fujian Natural Science Foundation under Grant 2015J05131, and the Fujian Provincial Key Laboratory of Eco-Industrial Green Technology

Fungal endophytes from arid areas of Andalusia: high potential sources for antifungal and antitumoral agents

Native plant communities from arid areas present distinctive characteristics to survive in extreme conditions. The large number of poorly studied endemic plants represents a unique potential source for the discovery of novel fungal symbionts as well as host-specific endophytes not yet described. The addition of adsorptive polymeric resins in fungal fermentations has been seen to promote the production of new secondary metabolites and is a tool used consistently to generate new compounds with potential biological activities. A total of 349 fungal strains isolated from 63 selected plant species from arid ecosystems located in the southeast of the Iberian Peninsula, were characterized morphologically as well as based on their ITS/28S ribosomal gene sequences. The fungal community isolated was distributed among 19 orders including Basidiomycetes and Ascomycetes, being Pleosporales the most abundant order. In total, 107 different genera were identified being Neocamarosporium the genus most frequently isolated from these plants, followed by Preussia and Alternaria. Strains were grown in four different media in presence and absence of selected resins to promote chemical diversity generation of new secondary metabolites. Fermentation extracts were evaluated, looking for new antifungal activities against plant and human fungal pathogens, as well as, cytotoxic activities against the human liver cancer cell line HepG2. From the 349 isolates tested, 126 (36%) exhibited significant bioactivities including 58 strains with exclusive antifungal properties and 33 strains with exclusive activity against the HepG2 hepatocellular carcinoma cell line. After LCMS analysis, 68 known bioactive secondary metabolites could be identified as produced by 96 strains, and 12 likely unknown compounds were found in a subset of 14 fungal endophytes. The chemical profiles of the differential expression of induced activities were compared. As proof of concept, ten active secondary metabolites only produced in the presence of resins were purified and identified. The structures of three of these compounds were new and herein are elucidated.This work was supported by Fundación MEDINA and the Andalusian Government grant RNM-7987 ‘Sustainable use of plants and their fungal parasites from arid regions of Andalucía for new molecules useful for antifungals and neuroprotectors’

Gaussian-valued particle swarm optimization

This paper examines the position update equation of the particle swarm optimization (PSO) algorithm, leading to the proposal of a simplified position update based upon a Gaussian distribution. The proposed algorithm, Gaussian-valued particle swarm optimization (GVPSO), generates probabilistic positions by retaining key elements of the canonical update procedure while also removing the need to specify values for the traditional PSO control parameters. Experimental results across a set of 60 benchmark problems indicate that GVPSO outperforms both the standard PSO and the bare bones particle swarm optimization (BBPSO) algorithm, which also employs a Gaussian distribution to generate particle positions.The National Research Foundation (NRF) of South Africa (Grant Number 46712) and the Natural Sciences and Engineering Research Council of Canada (NSERC).http://link.springer.combookseries/5582019-10-03hj2018Computer Scienc