Heavy metals in compostable wastes: A case study in Gumushane, Turkey

WOS: 000249956600012This paper presents a general overview of the current municipal solid waste management in Gumushane Province, Turkey. In the study conducted between March 2004 and February 2005, the composting parameters (moisture content, total organic carbon, total nitrogen and pH), organic matter content and the heavy metal concentrations (Cd, Cr, Cu, Ni, Pb, Zn, Fe, Mn, Co) of the green wastes sorted from the mixed municipal solid waste were determined and evaluated. The analytes were determined with a relative standard deviation lower than 6% in all samples. T e organic fraction of biological origin made up about 30% of the total amount of the municipal solid waste. Moisture content was 78%, organic matter content was 92.1%, C/N ratio was 21.6/1 and pH was 4.73. Heavy metal concentrations of the green wastes mixed with domestic wastes were found to be higher than the separately collected green wastes. Heavy metal concentrations of the green wastes from the open dump were determined to decrease in the following, order: Fe>Mn>Zn>Cr>Cu>Pb>Ni>Cd>Co

On the unsteady formation of secondary flow inside a rotating turbine blade passage

This paper addresses the unsteady formation of secondary flow structures inside a turbine rotor passage. The first stage of a two-stage, low-pressure turbine is investigated at a Reynolds Number of 75,000. The design represents the third and the fourth stages of an engine-representative, low-pressure turbine. The flow field inside the rotor passage is discussed in the relative frame of reference using the streamwise vorticity. A multistage unsteady Reynolds-averaged Navier–Stokes (URANS) prediction provides the time-resolved data set required. It is supported by steady and unsteady area traverse data acquired with five-hole probes and dual-film probes at rotor inlet and exit. The unsteady analysis reveals a nonclassical secondary flow field inside the rotor passage of this turbine. The secondary flow field is dominated by flow structures related to the upstream nozzle guide vane. The interaction processes at hub and casing appear to be mirror images and have characteristic forms in time and space. Distinct loss zones are identified, which are associated with vane-rotor interaction processes. The distribution of the measured isentropic stage efficiency at rotor exit is shown, which is reduced significantly by the secondary flow structures discussed. Their impacts on the steady as well as on the unsteady angle characteristics at rotor exit are presented to address the influences on the inlet conditions of the downstream nozzle guide vane. It is concluded that URANS should improve the optimization of rotor geometry and rotor loss can be controlled, to a degree, by nozzle guide vane (NGV) design

Nonlinear compact localized modes in flux-dressed octagonal-diamond lattice

Tuning the values of artificial flux in the two-dimensional octagonal-diamond lattice drives topological phase transitions, including between singular and non-singular flatbands. We study the dynamical properties of nonlinear compact localized modes that can be continued from linear flatband modes. We show how the stability of the compact localized modes can be tuned by the nonlinearity strength or the applied artificial flux. Our model can be realized using ring resonator lattices or nonlinear waveguide arrays.11Nsciescopu

A toolbox of machine learning software to support microbiome analysis

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.Peer reviewe